Support
Worldwide Technical Support and Product Information
ni.com
National Instruments Corporate Headquarters
11500 North Mopac Expressway Austin, Texas 78759-3504 USA Tel: 512 794 0100
Worldwide Offices
Australia 03 9879 5166, Austria 0662 45 79 90 0, Belgium 02 757 00 20, Brazil 011 284 5011,
Canada (Calgary) 403 274 9391, Canada (Ontario) 905 785 0085, Canada (Québec) 514 694 8521,
China 0755 3904939, Denmark 45 76 26 00, Finland 09 725 725 11, France 01 48 14 24 24,
Israel 03 6120092, Italy 02 413091, Japan 03 5472 2970, Korea 02 596 7456, Mexico (D.F.) 5 280 7625,
Mexico (Monterrey) 8 357 7695, Netherlands 0348 433466, New Zealand 09 914 0488, Norway 32 27 73 00,
Poland 0 22 528 94 06, Portugal 351 1 726 9011, Singapore 2265886, Spain 91 640 0085,
Sweden 08 587 895 00, Switzerland 056 200 51 51, Taiwan 02 2528 7227, United Kingdom 01635 523545
For further support information, see the Technical Support Resources appendix. To comment on the
documentation, send e-mail to [email protected]
Copyright © 2001 National Instruments Corporation. All rights reserved.
Download from Www.Somanuals.com. All Manuals Search And Download.
Conventions
The following conventions are used in this manual:
»
The » symbol leads you through nested menu items and dialog box options
to a final action. The sequence File»Page Setup»Options directs you to
pull down the File menu, select the Page Setup item, and select Options
from the last dialog box.
This icon denotes a note, which alerts you to important information.
bold
Bold text denotes items that you must select or click on in the software,
such as menu items and dialog box options. Bold text also denotes
parameter names.
italic
Italic text denotes variables, emphasis, a cross reference, or an introduction
to a key concept. This font also denotes text that is a placeholder for a word
or value that you must supply.
monospace
Text in this font denotes text or characters that you should enter from the
keyboard, sections of code, programming examples, and syntax examples.
This font is also used for the proper names of disk drives, paths, directories,
programs, subprograms, subroutines, device names, functions, operations,
variables, filenames and extensions, and code excerpts.
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 1
About IMAQ Vision ......................................................................................................1-1
Documentation and Examples .......................................................................................1-1
IMAQ Vision Function Tree..........................................................................................1-2
Creating IMAQ Vision Applications.............................................................................1-5
Chapter 2
Getting Measurement-Ready Images
Acquiring an Image.........................................................................................2-6
Converting an Array to an Image....................................................................2-7
Lookup Tables.................................................................................................2-9
Filters...............................................................................................................2-10
Convolution Filter.............................................................................2-10
Grayscale Morphology ....................................................................................2-11
Chapter 3
Define Regions of Interest .............................................................................................3-1
Interactively Defining Regions........................................................................3-1
Programmatically Defining Regions...............................................................3-6
Defining Regions with Masks .........................................................................3-6
Measure Grayscale Statistics .........................................................................................3-7
© National Instruments Corporation
v
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Contents
Comparing Colors........................................................................................... 3-8
Learning Color Information............................................................................ 3-9
Specifying the Color Information to Learn ...................................... 3-10
Chapter 4
Blob Analysis
Correct Image Distortion............................................................................................... 4-2
Create a Binary Image................................................................................................... 4-2
Improve the Binary Image............................................................................................. 4-3
Removing Unwanted Blobs ............................................................................ 4-3
Separating Touching Blobs............................................................................. 4-4
Improving Blob Shapes................................................................................... 4-4
Chapter 5
Machine Vision
Programmatically Defining Regions............................................................... 5-9
Find Measurement Points.............................................................................................. 5-9
Finding Points Using Pattern Matching.......................................................... 5-13
Defining a Search Area..................................................................... 5-15
Setting Matching Parameters and Tolerances................................... 5-16
Testing the Search Algorithm on Test Images ................................. 5-18
Using a Ranking Method to Verify Results...................................... 5-18
Finding Points Using Color Pattern Matching................................................ 5-18
Defining and Creating Good Color Template Images...................... 5-19
IMAQ Vision for LabWindows/CVI User Manual
vi
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Convert Pixel Coordinates to Real-World Coordinates.................................................5-26
Make Measurements......................................................................................................5-26
Analytic Geometry Measurements..................................................................5-27
Chapter 6
Perspective and Nonlinear Distortion Calibration.........................................................6-1
Defining a Calibration Template.....................................................................6-2
Learning the Error Map.....................................................................6-8
Learning the Correction Table ..........................................................6-8
Calibration Invalidation ....................................................................6-8
Simple Calibration .........................................................................................................6-9
Attach Calibration Information......................................................................................6-10
Appendix A
Technical Support Resources
Glossary
Index
© National Instruments Corporation
vii
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
1
Introduction to IMAQ Vision
This chapter describes the IMAQ Vision for LabWindows/CVI software
and associated software products, discusses the documentation and
examples available, outlines the IMAQ Vision function organization, and
lists the steps for making a machine vision application.
Note For information about the system requirements and installation procedure for
IMAQ Vision for LabWindows/CVI, see the IMAQ Vision for Measurement Studio
Release Notes that came with your software.
About IMAQ Vision
IMAQ Vision for Measurement Studio is a National Instruments product
comprised of IMAQ Vision for LabWindows/CVI and IMAQ Vision for
Visual Basic. IMAQ Vision for LabWindows/CVI is a library of C
functions that you can use to develop machine vision and scientific imaging
applications. IMAQ Vision for Visual Basic is a collection of ActiveX
controls that offer the same imaging functionality as IMAQ Vision for
LabWindows/CVI. National Instruments also offers IMAQ Vision for
LabVIEW, which is a library of LabVIEW VIs for developing machine
vision and scientific imaging applications. IMAQ Vision Builder, another
software product from National Instruments, allows you to prototype your
application strategy quickly without having to do any programming.
Documentation and Examples
In addition to this manual, several documentation resources are available
to help you create your vision application:
•
IMAQ Vision Concepts Manual—If you are new to machine vision
and imaging, read this manual to understand the concepts behind
IMAQ Vision.
•
IMAQ Vision for LabWindows/CVI function reference—If you need
information about IMAQ Vision functions while creating your
application, refer to this help file. Access this file from the Start menu
by selecting Programs»National Instruments»Vision»
© National Instruments Corporation
1-1
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 1
Introduction to IMAQ Vision
Documentation»IMAQ Vision for LabWindows/CVI Function
Reference.
•
•
Example programs—If you want examples of how to create specific
applications, go to cvi\samples\vision.
Application Notes—If you want to know more about advanced
IMAQ Vision concepts and applications, refer to the Application
Notes located on the National Instruments Web site at
ni.com/appnotes.nsf/
•
NI Developer Zone (NIDZ)—If you want even more information about
developing your vision application, visit the NI Developer Zone at
ni.com/zone. The NI Developer Zone contains example programs,
tutorials, technical presentations, the Instrument Driver Network, a
measurement glossary, an online magazine, a product advisor, and a
community area where you can share ideas, questions, and source code
with vision developers around the world.
Application Development Environments
This release of IMAQ Vision for LabWindows/CVI supports the following
Application Development Environments (ADEs) for Windows
2000/NT/Me/9x.
•
•
•
LabWindows/CVI version 5.0.1 and higher
Borland C++ Builder 3.0 and higher
Microsoft Visual C/C++ version 6.0 and higher
Note Although IMAQ Vision has been tested and found to work with these ADEs, other
ADEs may also work.
IMAQ Vision Function Tree
The IMAQ Vision function tree (NIVision.lfp) contains separate
classes corresponding to groups or types of functions. Table 1-1 lists the
IMAQ Vision function types and gives a description of each type.
IMAQ Vision for LabWindows/CVI User Manual
1-2
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 1
Introduction to IMAQ Vision
Table 1-1. IMAQ Vision Function Types
Description
Function Type
Image
Management
Functions that create space in memory for images and perform basic image
manipulation.
Memory
A function that returns memory you no longer need to the operating system.
Management
Error
Management
Functions that set the current error, return the name of the function in which the
last error occurred, return the error code of the last error, and clear any pending
errors.
Acquisition
Display
Functions that acquire images through an IMAQ hardware device.
Functions that cover all aspects of image visualization and image window
management.
Overlay
Functions that create and manipulate overlays.
Regions of
Interest
Functions that create and manipulate regions of interest.
File I/O
Functions that read and write images to and from files.
Calibration
Image Analysis
Functions that learn calibration information and correct distorted images.
Functions that compute the centroid of an image, profile of a line of pixels,
and the mean line profile. This type also includes functions that calculate the
pixel distribution and statistical parameters of an image.
Grayscale
Processing
Functions for grayscale image processing and analysis.
Binary
Functions for binary image processing and analysis.
Processing
Color
Functions for color image processing and analysis.
Processing
Pattern
Functions that learn patterns and search for patterns in images.
Matching
Caliper
Functions designed for gauging, measurement, and inspection applications.
Operators
Functions that perform arithmetic, logic, and comparison operations with
two images or with an image and a constant value.
Analytic
Functions that perform basic geometric calculations on an image.
Geometry
© National Instruments Corporation
1-3
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 1
Introduction to IMAQ Vision
Table 1-1. IMAQ Vision Function Types (Continued)
Function Type
Description
Frequency
Domain
Analysis
Functions for the extraction and manipulation of complex planes. Functions of
this type perform FFTs, inverse FFTs, truncation, attenuation, addition,
subtraction, multiplication, and division of complex images.
Barcode
LCD
A function that reads a barcode.
Functions that find and read seven-segment LCD characters.
Functions that return the arc information of a meter and read the meter.
Meter
Utilities
Functions that return structures, and a function that returns a pointer to
predefined convolution matrices.
Obsolete
Functions that are no longer necessary but may exist in older applications.
IMAQ Machine Vision Function Tree
The IMAQ Machine Vision function tree (NIMachineVision.fp)
contains separate classes corresponding to groups or types of functions.
Table 1-2 lists the IMAQ Machine Vision function types and gives a
description of each type.
Table 1-2. IMAQ Machine Vision Function Types
Function Type
Description
Coordinate Transform
Functions that find coordinate transforms based on image contents.
Count and Measure Objects A function that counts and measures objects in an image.
Find Patterns
A function that finds patterns in an image.
Locate Edges
Functions that locate different types of edges in an image.
Functions that measure distances between objects in an image.
Measure Distances
Measure Intensities
Functions that measure light intensities in various shaped regions
within an image.
Select Region of Interest
Functions that allow a user to select a specific region of interest in
an image.
IMAQ Vision for LabWindows/CVI User Manual
1-4
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 1
Introduction to IMAQ Vision
Creating IMAQ Vision Applications
Figures 1-1 and 1-2 illustrate the steps for creating an application with
IMAQ Vision. Figure 1-1 describes the general steps to designing a Vision
application. The last step in Figure 1-1 is expanded upon in Figure 1-2.
You can use a combination of the items in the last step to create your IMAQ
Vision application. For more information about items in either diagram,
see the corresponding chapter listed to the right of the item.
Note Diagram items enclosed with dashed lines are optional steps.
Set Up Your Imaging System
Chapter 6:
Calibration
Calibrate Your Imaging System
Create an Image
Acquire or Read an Image
Chapter 2:
Getting
Measurement-Ready
Images
Display an Image
Attach Calibration Information
Analyze an Image
Improve an Image
Make Measurements in an Image Using
Grayscale or Color Measurements, and/or
1
2
3
Blob Analysis, and/or
Machine Vision
Figure 1-1. General Steps to Designing a Vision Application
© National Instruments Corporation
1-5
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 1
Introduction to IMAQ Vision
1
Define Regions of Interest
Chapter 3:
Grayscale and Color
Measurements
Measure
Grayscale Statistics
Measure
Color Statistics
2
3
Locate Objects to Inspect
Set Search Areas
Correct Image Distortion
Create a Binary Image
Chapter 4:
Blob Analysis
Find Measurement Points
Improve a Binary Image
Chapter 5:
Machine Vision
Convert Pixel Coordinates to
Real-World Coordinates
Make Particle Measurements
Convert Pixel Coordinates to
Real-World Coordinates
Make Measurements
Display Results
Figure 1-2. Inspection Steps for Building a Vision Application
IMAQ Vision for LabWindows/CVI User Manual
1-6
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
2
Getting Measurement-Ready
Images
This chapter describes how to set up your imaging system, acquire and
display an image, analyze the image, and prepare the image for additional
processing.
Set Up Your Imaging System
Before you acquire, analyze, and process images, you must set up your
imaging system. The manner in which you set up your system depends on
your imaging environment and the type of analysis and processing you
need to do. Your imaging system should produce images with high enough
quality so that you can extract the information you need from the images.
Follow the guidelines below to setup your imaging system.
1. Determine the type of equipment you need given your space
constraints and the size of the object you need to inspect. For more
information, see Chapter 3, System Setup and Calibration, of the
IMAQ Vision Concepts Manual.
a. Make sure your camera sensor is large enough to satisfy your
minimum resolution requirement.
b. Make sure your lens has a depth of field high enough to keep all
of your objects in focus regardless of their distance from the lens.
Also, make sure your lens has a focal length that meets your
needs.
c. Make sure your lighting provides enough contrast between the
object under inspection and the background for you to extract the
information you need from the image.
2. Position your camera so that it is parallel to the object under
inspection. If your camera acquires images of the object from an angle,
perspective errors occur. You can compensate for these errors with
software, but using a parallel inspection angle obtains the fastest and
most accurate results.
© National Instruments Corporation
2-1
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Getting Measurement-Ready Images
3. Select an image acquisition device that meets your needs. National
Instruments offers several image acquisition (IMAQ) devices, such as
analog color and monochrome IMAQ devices as well as digital
devices. Visit ni.com/imaqfor more information about IMAQ
devices.
4. Configure the driver software for your image acquisition device. If
you have a National Instruments image acquisition device, configure
your NI-IMAQ driver software through Measurement & Automation
Explorer (MAX). Open MAX by double-clicking the Measurement &
Automation Explorer icon on your desktop. For more information,
see the NI-IMAQ User Manual and the MAX online help.
Calibrate Your Imaging System
After you set up your imaging system, you may want to calibrate your
system. Calibrate your imaging system to assign real-world coordinates to
pixel coordinates and compensate for perspective and nonlinear errors
inherent in your imaging system.
Perspective errors occur when your camera axis is not perpendicular to the
object under inspection. Nonlinear distortion may occur from aberrations
in the camera lens. Perspective errors and lens aberrations cause images to
does not necessarily destroy the information in the image.
Use simple calibration if you only want to assign real-world coordinates to
pixel coordinates. Use perspective and nonlinear distortion calibration if
you need to compensate for perspective errors and nonlinear lens distortion.
For detailed information about calibration, see Chapter 6, Calibration.
Create an Image
To create an image in IMAQ Vision for LabWindows/CVI, call
imaqCreateImage(). This function returns an image reference you can
use when calling other IMAQ Vision functions. The only limitation to the
size and number of images you can acquire and process is the amount of
memory on your computer. When you create an image, specify the type of
the image. Table 2-1 lists the valid image types.
IMAQ Vision for LabWindows/CVI User Manual
2-2
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Getting Measurement-Ready Images
Table 2-1. IMAQ Vision for LabWindows/CVI Image Types
Value
Description
8 bits per pixel—unsigned, standard monochrome
16 bits per pixel—signed, monochrome
IMAQ_IMAGE_U8
IMAQ_IMAGE_I16
IMAQ_IMAGE_SGL
IMAQ_IMAGE_COMPLEX
32 bits per pixel—floating point, monochrome
2 × 32 bits per pixel—floating point, native format after a Fast
Fourier Transform (FFT)
IMAQ_IMAGE_RGB
IMAQ_IMAGE_HSL
32 bits per pixel—standard color
32 bits per pixel—color
If you plan to use filtering or blob analysis functions on the image, set the
appropriate border size for the image. The default border size is 3.
When you create an image, IMAQ Vision creates an internal image
structure to hold properties of the image, such as its name and border size.
However, no memory is allocated to store the image pixels at this time.
IMAQ Vision functions automatically allocate the appropriate amount of
memory when the image size is modified. For example, functions that
acquire or resample an image alter the image size, so they allocate the
appropriate memory space for the image pixels. The return value of
imaqCreateImage()is a pointer to the image structure. Supply this
pointer as an input to all subsequent IMAQ Vision functions.
Most functions belonging to the IMAQ Vision library require one or more
image pointers. The number of image pointers a function takes depends on
the image processing function and the type of image you want to use. Some
IMAQ Vision functions act directly on the image and require only one
image pointer. Other functions that process the contents of images require
pointers to the source image(s) and to a destination image.
You can create multiple images by executing imaqCreateImage()as
many times as you want. Determine the number of required images through
an analysis of your intended application. The decision is based on different
processing phases and your need to keep the original image (after each
processing step).
At the end of your application, dispose of each image that you created using
imaqDispose().
© National Instruments Corporation
2-3
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Getting Measurement-Ready Images
Source and Destination Images
Some IMAQ Vision functions that modify the contents of an image have
source image and destination image input parameters. The source image
receives the image to process. The destination image receives the
processing results. The destination image can receive either another image
or the original, depending on your goals. If you do not want the contents of
the original image to change, use separate source and destination images.
If you want to replace the original image with the processed image, pass the
same image as both the source and destination.
Depending on the function, the image type of the destination image can be
the same or different than the image type of the source image. The function
descriptions in the IMAQ Vision for LabWindows/CVI function reference
help include the type of images you can use as image inputs and outputs.
IMAQ Vision resizes the destination image to hold the result if the
destination is not the appropriate size.
The following examples illustrate source and destination images with
imaqTranspose():
• imaqTranspose(myImage,myImage);
This function creates a transposed image using the same image for the
source and destination. The contents of myImagechange.
• imaqTranspose(myTransposedImage,myImage);
This function creates a transposed image and stores it in a destination
different from the source. The myImageimage remains unchanged,
and myTransposedImagecontains the result.
Functions that perform arithmetic or logical operations between two
images have two source images and a destination image. You can perform
an operation between two images and then either store the result in a
separate destination image or in one of the two source images. In the
latter case, make sure you no longer need the original data in the source
image before storing the result over the data.
The following examples show the possible combinations using
imaqAdd():
• imaqAdd(myResultImage,myImageA,myImageB);
This function adds two source images (myImageAand myImageB) and
images remain intact after processing.
IMAQ Vision for LabWindows/CVI User Manual
2-4
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Getting Measurement-Ready Images
• imaqAdd(myImageA,myImageA,myImageB);
This function adds two source images and stores the result in the first
source image.
• imaqAdd(myImageB,myImageA,myImageB);
This function adds two source images and stores the result in the
second source image.
Most operations between two images require that the images have the
same type and size. However, some arithmetic operations can work
between two different types of images (for example, 8-bit and 16-bit).
Some functions perform operations that populate an image. Examples of
this type of operation include reading a file, acquiring an image from an
IMAQ device, or transforming a 2D array into an image. This type of
function can modify the size of an image.
Some functions take an additional mask parameter. The presence of a
mask parameter indicates that the processing or analysis is dependent on
the contents of another image (the image mask). The only pixels in the
source image that are processed are those whose corresponding pixels in
the image mask are non-zero. If an image mask pixel is 0, the
corresponding source image pixel is not processed or analyzed. The image
mask must be an 8-bit image.
If you want to apply a processing or analysis function to the entire image,
pass NULL for the image mask. Passing the same image to both the source
image and image mask also gives the same effect as passing NULL for the
image mask, except in this case the source image must be an 8-bit image.
Acquire or Read an Image
After you create an image reference, you can acquire an image into your
imaging system in three ways. You can acquire an image with a camera
through your image acquisition device, load an image from a file stored on
your computer, or convert the data stored in a 2D array to an image.
Functions that acquire images, load images from file, or convert data from
a 2D array automatically allocate the memory space required to
accommodate the image data.
© National Instruments Corporation
2-5
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Getting Measurement-Ready Images
Acquiring an Image
Use one of the following methods to acquire images with a National
Instruments image acquisition (IMAQ) device:
•
Acquire a single image using imaqEasyAcquire(). When you call
this function, it initializes the IMAQ device and acquires the next
incoming video frame. Use this function for low-speed single capture
applications where ease of programming is essential.
•
•
•
Acquire a single image using imaqSnap(). When you call this
function, it acquires the next incoming video frame on an IMAQ
device you have already initialized using imgInterfaceOpen()and
imgSessionOpen(). Use this function for high-speed single capture
applications.
Acquire images continually through a grab acquisition. Grab functions
perform high-speed acquisitions that loop continually on one buffer.
Use imaqSetupGrab()to start the acquisition. Use imaqGrab()to
return a copy of the current image. Use imaqStopAcquisition()to
stop the acquisition.
Acquire a fixed number of images using a sequence acquisition. Set up
the acquisition using imaqSetupSequence(). Use
imaqStartAcquisition()to acquire the number of images you
requested during setup. If you want to acquire only certain images,
supply imaqSetupSequence()with a table describing the number of
frames to skip after each acquired frame.
•
Acquire images continually through a ringed buffer acquisition. Set up
the acquisition using imaqSetupRing(). Use
imaqStartAcquisition()to start acquiring images into the
acquired ring buffer. To get an image from the ring, call
imaqExtractFromRing()or imaqCopyRing(). Use
imaqStopAcquisition()to stop the acquisition.
Note You must use the imgClose()to release resources associated with the image
acquisition device.
Reading a File
Use imaqReadFile()to open and read data from a file stored on your
computer into the image reference. You can read from image files stored in
several standard formats: BMP, TIFF, JPEG, PNG, and AIPD. The
software automatically converts the pixels it reads into the type of image
you pass in.
IMAQ Vision for LabWindows/CVI User Manual
2-6
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Use imaqReadVisionFile()to open an image file containing additional
information, such as calibration information, template information for
pattern matching, or overlay information. For more information about
pattern matching templates and overlays, see Chapter 5, Machine Vision.
You can also use imaqGetFileInfo()to retrieve image
properties—image size, recommended image type, and calibration
units—without actually reading all the image data.
Converting an Array to an Image
Use imaqArrayToImage()to convert a 2D array to an image. You can
also use imaqImageToArray()to convert an image to a 2D array.
Display an Image
Display an image in an external window using imaqDisplayImage().
You can display images in 16 different external windows. Use the other
display functions to configure the appearance of each external window.
Properties you can set include whether the window has scroll bars, is
resizable, or has a title bar. You can also use imaqMoveWindow()to
position the external image window at a particular location on you monitor.
See the IMAQ Vision for LabWindows/CVI function reference for a
complete list of display functions.
Note Image windows are not LabWindows/CVI panels. They are managed directly by
IMAQ Vision.
You can use a color palette to display grayscale images by applying a color
palette to the window. Use imaqSetWindowPalette()to set predefined
color palettes. For example, if you need to display a binary image—an
image containing particle regions with pixel values of 1 and a background
region with pixel values of 0—apply the predefined binary palette. For
more information about color palettes, see the Chapter 2, Display, of the
IMAQ Vision Concepts Manual.
Note At the end of your application, you should close all open external windows using
imaqCloseWindow().
© National Instruments Corporation
2-7
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Getting Measurement-Ready Images
Attach Calibration Information
If you want to attach the calibration information of the current setup to
each image you acquire, use imaqCopyCalibrationInfo(). This
function takes in a source image containing the calibration information and
a destination image that you want to calibrate. The output image is your
inspection image with the calibration information attached to it. For
detailed information about calibration, see Chapter 6, Calibration.
Note Because calibration information is part of the image, it is propagated throughout
the processing and analysis of the image. Functions that modify the image size (such as
geometrical transforms) void the calibration information. Use imaqWriteVisionFile()
to save the image and all of the attached calibration information to a file.
Analyze an Image
Once you acquire and display an image, you may want to analyze the
contents of the image for the following reasons:
•
To determine whether the image quality is high enough for your
inspection task.
•
To obtain the values of parameters that you want to use in processing
functions during the inspection process.
The histogram and line profile tools can help you analyze the quality of
your images.
Use imaqHistogram()to analyze the overall grayscale distribution in the
image. Use the histogram of the image to analyze two important criteria
that define the quality of an image—saturation and contrast. If your image
is underexposed (does not have enough light) the majority of your pixels
will have low intensity values, which appear as a concentration of peaks on
the left side of your histogram. If your image is overexposed (has too much
light) the majority of your pixels will have a high intensity values, which
appear as a concentration of peaks on the right side of your histogram. If
your image has an appropriate amount of contrast, your histogram will have
distinct regions of pixel concentrations. Use the histogram information to
decide if the image quality is high enough to separate objects of interest
from the background.
If the image quality meets your needs, use the histogram to determine the
range of pixel values that correspond to objects in the image. You can use
IMAQ Vision for LabWindows/CVI User Manual
2-8
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Getting Measurement-Ready Images
this range in processing functions, such as determining a threshold range
during blob analysis.
If the image quality does not meet your needs, try to improve the imaging
and modify each component of your imaging setup: lighting equipment
and setup, lens tuning, camera operation mode, and acquisition board
parameters. If you reach the best possible conditions with your setup but
the image quality still does not meet your needs, try to improve the image
quality using the image processing techniques described in the Improve an
Image section.
Use imaqLineProfile()to get the pixel distribution along a line in the
image, or use imaqROIProfile()to get the pixel distribution along a
one-dimensional path in the image. By looking at the pixel distribution, you
can determine if the image quality is high enough to provide you with sharp
edges at object boundaries. Also, you can determine if the image is noisy,
and identify the characteristics of the noise.
If the image quality meets your needs, use the pixel distribution information
to determine some parameters of the inspection functions you want to use.
For example, use the information from the line profile to determine the
strength of the edge at the boundary of an object. You can input this
information into imaqEdgeTool()to find the edges of objects along the
line.
Improve an Image
Using the information you gathered from analyzing your image, you may
want to improve the quality of your image for inspection. You can improve
your image with lookup tables, filters, grayscale morphology, and Fast
Fourier transforms.
Lookup Tables
Apply lookup table (LUT) transformations to highlight image details in
areas containing significant information at the expense of other areas.
A LUT transformation converts input grayscale values in the source image
into other grayscale values in the transformed image. IMAQ Vision
provides four functions that directly or indirectly apply lookup tables to
images:
• imaqMathTransform()—Converts the pixel values of an image by
replacing them with values from a predefined lookup table. IMAQ
Vision has seven predefined lookup tables based on mathematical
© National Instruments Corporation
2-9
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Getting Measurement-Ready Images
transformations. For more information about these lookup tables, see
Chapter 5, Image Processing, in the IMAQ Vision Concepts Manual.
• imaqLookup()—Converts the pixel values of an image by replacing
them with values from a user-defined lookup table.
• imaqEqualize()—Distributes the grayscale values evenly within a
given grayscale range. Use IMAQ Equalize to increase the contrast in
images containing few grayscale values.
• imaqInverse()—Inverts the pixel intensities of an image to
compute the negative of the image. For example, use imaqInverse()
before applying an automatic threshold to your image if the
background pixels are brighter than the object pixels.
Filters
Filter your image when you need to improve the sharpness of transitions in
the image or increase the overall signal-to-noise ratio of the image. You can
choose either a lowpass or highpass filter depending on your needs.
Lowpass filters remove insignificant details by smoothing the image,
removing sharp details, and smoothing the edges between the objects
and the background. You can use imaqLowpass()or define your own
lowpass filter with imaqConvolve()or imaqNthOrderFilter().
Highpass filters emphasize details, such as edges, object boundaries, or
cracks. These details represent sharp transitions in intensity value. You can
define your own highpass filter with imaqConvolve()or
imaqNthOrderFilter(), or you can use a predefined highpass filter with
imaqEdgeFilter()or imaqCannyEdgeFilter(). The
imaqEdgeFilter()function allows you to find edges in an image using
predefined edge detection kernels, such as the Sobel, Prewitt, and Roberts
kernels.
Convolution Filter
The imaqConvolve()function allows you to use a predefined set of
lowpass and highpass filters. Each filter is defined by a kernel of
coefficients. Use imaqGetKernel()to retrieve predefined kernels. If the
predefined kernels do not meet your needs, define your own custom filter
using a 2D array of floating point numbers.
IMAQ Vision for LabWindows/CVI User Manual
2-10
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Getting Measurement-Ready Images
Nth Order Filter
The imaqNthOrderFilter()function allows you to define a lowpass or
highpass filter depending on the value of N that you choose. One specific
Nth order filter, the median filter, removes speckle noise, which appears as
small black and white dots. Use imaqMedianFilter()to apply a median
filter. For more information about Nth order filters, see Chapter 5, Image
Processing, of the IMAQ Vision Concepts Manual.
Grayscale Morphology
Perform grayscale morphology when you want to filter grayscale
features of an image. Grayscale morphology helps you remove or
enhance isolated features, such as bright pixels on a dark background.
Use these transformations on a grayscale image to enhance non-distinct
features before thresholding the image in preparation for blob analysis.
Use imaqGrayMorphology()to perform one of the following seven
transformations:
•
•
•
•
•
•
•
Erosion—Reduces the brightness of pixels that are surrounded by
neighbors with a lower intensity.
Dilation—Increases the brightness of pixels surrounded by neighbors
with a higher intensity. A dilation has the opposite effect as an erosion.
Opening—Removes bright pixels isolated in dark regions and smooths
boundaries.
Closing—Removes dark pixels isolated in bright regions and smooths
boundaries.
Proper-opening—Removes bright pixels isolated in dark regions and
smooths the inner contours of particles.
Proper-closing—Removes dark pixels isolated in bright regions and
smooths the inner contours of particles.
Auto-median—Generates simpler particles that have fewer details.
For more information about grayscale morphology transformations, see
Chapter 5, Image Processing, of the IMAQ Vision Concepts Manual.
FFT
Use the Fast Fourier Transform (FFT) to convert an image into its
frequency domain. In an image, details and sharp edges are associated
with mid to high spatial frequencies because they introduce significant
gray-level variations over short distances. Gradually varying patterns are
associated with low spatial frequencies.
© National Instruments Corporation
2-11
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Getting Measurement-Ready Images
An image can have extraneous noise, such as periodic stripes, introduced
during the digitization process. In the frequency domain, the periodic
pattern is reduced to a limited set of high spatial frequencies. Also, the
imaging setup may produce non-uniform lighting of the field of view,
which produces an image with a light drift superimposed on the
information you want to analyze. In the frequency domain, the light drift
appears as a limited set of low frequencies around the average intensity of
the image (DC component).
You can use algorithms working in the frequency domain to isolate and
remove these unwanted frequencies from your image. Follow these steps to
obtain an image in which the unwanted pattern has disappeared but the
overall features remain:
1. Use imaqFFT()to convert an image from the spatial domain to the
frequency domain. This function computes the Fast Fourier Transform
(FFT) of the image and results in a complex image representing the
frequency information of your image.
2. Improve your image in the frequency domain with a lowpass or
highpass frequency filter. Specify which type of filter to use with
imaqAttenuate()or imaqTruncate(). Lowpass filters smooth
noise, details, textures, and sharp edges in an image. Highpass filters
emphasize details, textures, and sharp edges in images, but they also
emphasize noise.
•
Lowpass attenuation—The amount of attenuation is directly
proportional to the frequency information. At low frequencies,
there is little attenuation. As the frequencies increase, the
attenuation increases. This operation preserves all of the zero
frequency information. Zero frequency information corresponds
to the DC component of the image or the average intensity of
the image in the spatial domain.
•
Highpass attenuation—The amount of attenuation is inversely
proportional to the frequency information. At high frequencies,
there is little attenuation. As the frequencies decrease, the
attenuation increases. The zero frequency component is removed
entirely.
•
Lowpass truncation—Specify a frequency. The frequency
components above the ideal cutoff frequency are removed, and the
frequencies below it remain unaltered.
components above the ideal cutoff frequency remain unaltered,
and the frequencies below it are removed.
IMAQ Vision for LabWindows/CVI User Manual
2-12
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 2
Getting Measurement-Ready Images
3. To transform your image back to the spatial domain, use
imaqInverseFFT().
Complex Image Operations
The imaqExtractComplexPlane()and
imaqReplaceComplexPlane()functions allow you to access, process,
and update independently the real and imaginary planes of a complex
image. You can also convert planes of a complex image to an array
and back with imaqComplexPlaneToArray()and
imaqArrayToComplexPlane().
© National Instruments Corporation
2-13
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
3
Grayscale and Color
Measurements
images. You can make inspection decisions based on image statistics, such
as the mean intensity level in a region. Based on the image statistics, you
can perform many machine vision inspection tasks on grayscale or color
images, such as detecting the presence or absence of components, detecting
flaws in parts, and comparing a color component with a reference.
Figure 3-1 illustrates the basic steps involved in making grayscale and
color measurements.
Define Regions of Interest
Measure
Measure
Grayscale Statistics
Color Statistics
Figure 3-1. Steps to Taking Grayscale and Color Measurements
Define Regions of Interest
A region of interest (ROI) is an area of an image in which you want to
focus your image analysis. You can define an ROI interactively,
programmatically, or with an image mask.
Interactively Defining Regions
You can interactively define an ROI in a window that displays an image.
Use the tools from the IMAQ Vision tools palette to interactively define and
manipulate an ROI. Table 3-1 describes each of the tools and the manner in
which you use them.
© National Instruments Corporation
3-1
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
Table 3-1. Tools Palette Functions
Icon
Tool Name
Function
Selection Tool
Select an ROI in the image and adjust the position of its control
points and contours.
Action: Click on the desired ROI or control points.
Select a pixel in the image.
Point
Line
Action: Click on the desired position.
Draw a line in the image.
Action: Click on the initial position and click again on the final
position.
Rectangle
Draw a rectangle or square in the image.
Action: Click on one corner and drag to the opposite corner.
Draw a rotated rectangle in the image.
Rotated Rectangle
Action: Click on one corner and drag to the opposite corner to
create the rectangle. Then, click on the lines inside the rectangle
and drag to adjust the rotation angle.
Oval
Draw an oval or circle in the image.
Action: Click on the center position and drag to the desired size.
Draw an annulus in the image.
Annulus
Action: Click on the center position and drag to the desired size.
Adjust the inner and outer radii, and adjust the start and end
angle.
Broken Line
Polygon
Draw a broken line in the image.
Action: Click to place a new vertex and double-click to complete
the ROI element.
Draw a polygon in the image.
Action: Click to place a new vertex and double-click to complete
the ROI element.
Freehand Line
Draw a freehand line in the image.
Action: Click on the initial position, drag to the desired shape and
release the mouse button to complete the shape.
IMAQ Vision for LabWindows/CVI User Manual
3-2
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
Table 3-1. Tools Palette Functions (Continued)
Icon
Tool Name
Freehand
Function
Draw a freehand region in the image.
Action: Click on the initial position, drag to the desired shape and
release the mouse button to complete the shape.
Zoom
Pan
Zoom-in or zoom-out in an image.
Action: Click on the image to zoom in. Hold down <Shift> and
click to zoom out.
Pan around an image.
Action: Click on an initial position, drag to the desired position
and release the mouse button to complete the pan.
Hold down <Shift> while drawing an ROI if you want to constrain the ROI
to the horizontal, vertical, or diagonal axes when possible. Use the selection
tool to position an ROI by its control points or vertices. ROIs are context
sensitive, meaning that the cursor actions differ depending on the ROI with
which you interact. For example, if you move your cursor over the side of
a rectangle, the cursor changes to indicate that you can click and drag the
side to resize the rectangle. If you want to draw more than one ROI in a
window, hold down <Ctrl> while drawing additional ROIs.
You can display the IMAQ Vision tools palette as part of an ROI constructor
window or in a separate, floating window. Follow these steps to invoke an
ROI constructor and define an ROI from within the ROI constructor
window:
1. Use imaqConstructROI()to display an image and the tools palette
in an ROI constructor window, as shown in Figure 3-2.
© National Instruments Corporation
3-3
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
Figure 3-2. ROI Constructor
2. Select an ROI tool from the tools palette.
3. Draw an ROI on your image. Resize and reposition the ROI until it
designates the area you want to inspect.
You can input the ROI descriptor into many analysis and processing
functions. You can also convert the ROI descriptor into an image mask,
which you can use to process selected regions in the image. Use
imaqROIToMask()to convert the ROI descriptor into an image mask.
The tools palette, shown in Figure 3-3, automatically transforms from the
palette on the left to the palette on the right when you manipulate an ROI
tool in an image window. The palette on the right displays the
characteristics of the ROI you are drawing.
IMAQ Vision for LabWindows/CVI User Manual
3-4
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
Pixel Intensity
Image-type indicator (8-bit, 16-bit, Float, RGB, HSL, Complex)
Coordinates of the mouse
on the active image window
Anchoring coordinates of a Region of Interest
Size of an active Region of Interest
Length and horizontal angle
of a line region
Figure 3-3. Tools Palette Tools and Information
The following list describes how you can display the tools palette in a
separate window and manipulate the palette.
•
Use imaqShowToolWindow()to display the tools window in a
floating window.
•
Use imaqSetupToolWindow()to configure the appearance of the
tools window.
•
•
Use imaqMoveToolWindow()to move the tools palette.
Use imaqCloseToolWindow()to close the tools palette.
If you want to draw an ROI without using an ROI constructor or displaying
the tools palette in a separate window, use imaqSetCurrentTool().
This function allows you to select a contour from the tools palette without
opening the palette.
© National Instruments Corporation
3-5
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
You can also use imaqSelectPoint(), imaqSelectLine(),
imaqSelectRect(), and imaqSelectAnnulus()to define regions of
interest. Follow these steps to use these functions:
1. Call the function to display an image in an ROI Constructor window.
Only the tools specific to that function are available for you to use.
2. Draw an ROI on your image. Resize or reposition the ROI until it
covers the area you want to process.
3. Click the OK button to populate a structure representing the ROI. You
can use this structure as an input to a variety of functions, such as the
following functions that measure grayscale intensity:
• imaqLightMeterPoint()—Uses the output of
imaqSelectPoint()
• imaqLightMeterLine()—Uses the output of
imaqSelectLine()
• imaqLightMeterRect()—Uses the output of
imaqSelectRect()
Programmatically Defining Regions
When you have an automated application, you may need to define regions
of interest programmatically. To programmatically define an ROI, create
the ROI using imaqCreateROI()and then add the individual contours.
A contour is a shape that defines an ROI. You can create contours from
points, lines, rectangles, ovals, polygons, and annuli. For example, to add a
rectangular contour to an ROI, use imaqAddRectContour().
Specify regions by providing basic parameters that describe the region you
want to define. For example, define a point by providing the x-coordinate
and y-coordinate. Define a line by specifying the start and end coordinates.
Define a rectangle by specifying the coordinates of the top, left point; the
width and height; and the rotation angle (in the case of a rotated rectangle).
Defining Regions with Masks
You can define regions to process with image masks. An image mask is
an 8-bit image of the same size as or smaller than the image you want to
process. Pixels in the mask image determine whether the corresponding
pixel in the source image needs to be processed. If a pixel in the image
mask has a value different than 0, the corresponding pixel in the source
image is processed. If a pixel in the image mask has a value of 0, the
corresponding pixel in the source image is left unchanged.
IMAQ Vision for LabWindows/CVI User Manual
3-6
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
When you need to make intensity measurements on particles in an image,
you can use a mask to define the particles. First, threshold your image to
make a new binary image. For more information on binary images, see
Chapter 4, Blob Analysis. You can input the binary image or a labeled
version of the binary image as a mask image to the intensity measurement
function. If you want to make color comparisons, convert the binary image
into an ROI descriptor using imaqMaskToROI().
Measure Grayscale Statistics
You can measure grayscale statistics in images using light meters or
quantitative analysis functions. You can obtain the center of energy for an
image with the centroid function.
Use imaqLightMeterPoint()to measure the light intensity at a point in
the image. Use imaqLightMeterLine()to get pixel value statistics
along a line in the image, such as mean intensity, standard deviation,
minimum intensity, and maximum intensity. Use
imaqLightMeterRect()to get the pixel value statistics within a
rectangular region in an image.
Use imaqQuantify()to obtain the following statistics about the entire
image or individual regions in the image: mean intensity, standard
deviation, minimum intensity, maximum intensity, area, and the percentage
of the image that you analyzed. You can specify regions in the image with
a labeled image mask. A labeled image mask is a binary image that has
been processed so that each region in the image mask has a unique intensity
value. Use imaqLabel()to label your image mask.
Use imaqCentroid()to compute the energy center of the image, or of a
region within an image.
Measure Color Statistics
Most image processing and analysis functions apply to 8-bit images.
However, you can analyze and process individual components of a color
image.
Using imaqExtractColorPlanes(), you can break down a color image
into various sets of primary components, such as RGB (Red, Green, and
Blue), HSI (Hue, Saturation, and Intensity), HSL (Hue, Saturation, and
Luminance), or HSV (Hue, Saturation, and Value). Each component
becomes an 8-bit image that you can process like any other grayscale
© National Instruments Corporation
3-7
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
image. Using imaqReplaceColorPlanes(), you can reassemble a color
image from a set of three 8-bit images, where each image becomes one of
the three primary components. Figure 3-4 illustrates how a color image
breaks down into its three components.
Red
Red
8
8
8
8
8
8
8
8
8
8
8
8
8
Green
Blue
Green
Blue
8
8
8
8
8
8
8
8
8
8
8
Hue
Hue
Saturation
Intensity
or
or Saturation
Intensity
Color
Color
Image
Image
32
32
8-bit Image Processing
Hue
Hue
Saturation
Luminance
Hue
or
Saturation
or
Luminance
Hue
or
Saturation
Saturation or
Value
Value
Figure 3-4. Primary Components of a Color Image
Use imaqExtractColorPlanes()to extract the red, green, blue, hue
saturation, intensity, luminance, or value plane of a color image into an
8-bit image.
Comparing Colors
You can use the color matching capability of IMAQ Vision to compare or
evaluate the color content of an image or regions in an image.
Follow these steps to compare colors using color matching:
as a reference. The color information can consist of a single color or
multiple dissimilar colors, such as red and blue.
IMAQ Vision for LabWindows/CVI User Manual
3-8
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
2. Use the entire image or regions in the image to learn the color
information using imaqLearnColor(), which outputs a color
spectrum that contains a compact description of the color information
that you learned. Use the color spectrum to represent the learned color
information for all subsequent matching operations. See Chapter 14,
Color Inspection, of the IMAQ Vision Concepts Manual for more
information.
3. Define an entire image, a region, or multiple regions in an image as the
inspection or comparison area.
4. Use imaqMatchColor()to compare the learned color information to
the color information in the inspection regions. This function returns
an array of scores that indicates how close the matches are to the
learned color information.
5. Use the color matching score as a measure of similarity between the
reference color information and the color information in the image
regions being compared.
Learning Color Information
When learning color information, you should choose the color information
carefully:
•
Specify an image or regions in an image that contain the color or color
set that you want to learn.
•
•
Select how detailed you want the color information to be learned.
Choose colors that you want to ignore during matching.
Choosing the Right Color Information
Because color matching only uses color information to measure similarity,
the image or regions in the image representing the object should contain
only the significant colors that represent the object, as shown in
Figure 3-5a. Figure 3-5b illustrates an unacceptable region containing
background colors.
© National Instruments Corporation
3-9
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
a.
b.
Figure 3-5. Template Color Information
Specifying the Color Information to Learn
You can learn the color information associated with an entire image,
a region in an image, or multiple regions in an image.
Using the Entire Image
You can use an entire image to learn the color spectrum that represents the
entire color distribution of the image. In a fabric identification application,
for example, an entire image can specify the color information associated
with a certain fabric type, as shown in Figure 3-6.
Using a Region in the Image
You can select a region in the image to provide the color information for
comparison. A region is helpful for pulling out the useful color information
in an image. Figure 3-7 shows an example of using a region that contains
the color information that is important for the application.
IMAQ Vision for LabWindows/CVI User Manual
3-10
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
Figure 3-7. Using a Single Region to Learn Color Distribution
Using Multiple Regions in the Image
The interaction of light with an object’s surface creates the observed
color of that object. The color of a surface depends on the directions of
illumination and the direction from which the surface is observed. Two
identical objects may have different appearances because of a difference in
positioning or a change in the lighting conditions. Figure 3-8 shows how
light reflects differently off of the 3D surfaces of the fuses, resulting in
slightly different colors for identical fuses. (Compare the 3 amp fuse in the
upper row with the 3 amp fuse in the lower row.) This results in different
color spectra for identical fuses.
If you learn the color spectrum by drawing a region of interest around the
3 amp fuse in the upper row, and then do a color matching for the 3 amp
fuse in the upper row, you get a very high match score for it (close to 1000).
But the match score for the 3 amp fuse in the lower row is quite low (around
500). This problem could cause a mismatch for the color matching in a fuse
box inspection process.
IMAQ Vision’s color learning software uses a clustering process to find
the representative colors from the color information specified by one or
multiple regions in the image. To create a representative color spectrum for
all 3 amp fuses in the learning phase, draw an ROI around the 3 amp fuse
in the upper row, hold down <Shift>, and draw another ROI around the 3
amp fuse in the lower row. The new color spectrum results in similar, high
match scores (around 800) for both 3 amp fuses. Use as many samples as
you want in an image to learn the representative color spectrum for a
specified template.
© National Instruments Corporation
3-11
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
1
1
Regions used to learn color information
Figure 3-8. Using Multiple Regions to Learn Color Distribution
Choosing a Color Representation Sensitivity
When you learn a color, you need to specify the sensitivity required to
colors in the color space requires a lower sensitivity to describe the color
than an image that contains colors that are close to one another in the color
space. Use the color sensitivity parameter of imaqLearnColor()to
specify the granularity you want to use to represent the colors. For more
information about color sensitivity, see the Color Sensitivity section of
Chapter 5, Machine Vision.
IMAQ Vision for LabWindows/CVI User Manual
3-12
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 3
Grayscale and Color Measurements
Ignoring Learned Colors
Ignore certain color components in color matching by replacing the
corresponding component in the input color spectrum array to –1. For
example, by replacing the last component in the color spectrum with –1,
color matching ignores the color white. By replacing the second to last
component in the color spectrum, color matching ignores the color black.
To ignore other color components in color matching, determine the index
to the color spectrum by locating the corresponding bins in the color wheel,
where each bin corresponds to a component in the color spectrum array.
Ignoring certain colors such as the background color results in a more
accurate color matching score. Ignoring the background color also provides
more flexibility when defining the regions of interest in the color matching
process. Ignoring certain colors, such as white color created by glare on a
metallic surface, also improves the accuracy of the color matching.
Experiment learning the color information on different parts of the images
to determine which colors to ignore. For more information about the color
wheel and color bins, see Chapter 14, Color Inspection, in the IMAQ Vision
Concepts Manual.
© National Instruments Corporation
3-13
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
4
Blob Analysis
This chapter describes how to perform blob (Binary Large Object) analysis
on your images. Use blob analysis to find statistical information about
blobs, such as the presence, size, number, and location of blob regions.
tasks, such as detecting flaws on silicon wafers or detecting soldering
defects on electronic boards. Examples of how blob analysis can help you
perform web inspection tasks include locating structural defects on wood
planks or detecting cracks on plastic sheets.
Figure 4-1 illustrates the steps involved in performing blob analysis.
Diagram items enclosed with dashed lines are optional steps.
Correct Image Distortion
Create a Binary Image
Improve a Binary Image
Make Particle Measurements
Convert Pixel Coordinates to
Real-World Coordinates
Figure 4-1. Steps to Performing Blob Analysis
© National Instruments Corporation
4-1
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 4
Blob Analysis
Correct Image Distortion
If you need to make accurate shape measurements based on the blobs in
an image containing perspective and nonlinear distortion errors, correct
the distortion using the calibration information you attached to your
image. Use imaqCorrectCalibratedImage()to correct distortion in
your grayscale image before thresholding it. See Chapter 6, Calibration,
for more information about correcting an image using calibration
information.
Create a Binary Image
Threshold your grayscale or color image to create a binary image. Creating
a binary image separates the objects that you want to inspect from the
background. The threshold operation sets the background pixels to 0 in the
binary image, while setting the object pixels to a non-zero value. Object
pixels have a value of 1 by default, but you can set the object pixels to any
value or retain their original value.
You can use different techniques to threshold your image. If all the
objects of interest in your grayscale image fall within a continuous range
of intensities and you can specify this threshold range manually, use
imaqThreshold()to threshold your image.
If all the objects in your grayscale image are either brighter or darker than
your background, you can use imaqAutoThreshold()to automatically
determine the optimal threshold range and threshold your image.
Automatic thresholding techniques offer more flexibility than simple
thresholds based on fixed ranges. Because automatic thresholding
techniques determine the threshold level according to the image histogram,
the operation is more independent of changes in the overall brightness and
contrast of the image than a fixed threshold. These techniques are more
resistant to changes in lighting, which makes them well suited for
automated inspection tasks.
If your grayscale image contains objects that have multiple discontinuous
grayscale values, use imaqMultithreshold()to specify multiple
threshold ranges.
If you need to threshold a color image, use imaqColorThreshold().
Green, and Blue or Hue, Saturation, and Luminance). The binary image
resulting from a color threshold is an 8-bit binary image.
IMAQ Vision for LabWindows/CVI User Manual
4-2
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 4
Blob Analysis
Improve the Binary Image
After you threshold your image, you may want to improve the resulting
binary image with binary morphology. You can use primary binary
morphology or advanced binary morphology to remove unwanted blobs,
separate connected blobs, or improve the shape of blobs. Primary
morphology functions work on the image as a whole by processing pixels
individually. Advanced morphology operations are built upon the primary
morphological operators and work on particles as opposed to pixels.
Note The terms blob and particle are used interchangeably in this chapter.
The advanced morphology functions that improve binary images require
that you specify the type of connectivity to use. Connectivity specifies how
IMAQ Vision determines whether two adjacent pixels belong to the same
particle. If you have a blob that contains narrow areas, use connectivity-8
to ensure that the software recognizes the connected pixels as one blob.
If you have two blobs that touch at one point, use connectivity-4 to ensure
that the software recognizes the pixels as two separate blobs. For more
information about connectivity, see Chapter 9, Binary Morphology, of the
IMAQ Vision Concepts Manual.
Note Use the same type of connectivity throughout your application.
Removing Unwanted Blobs
Use imaqRejectBorder()to remove blobs that touch the border of the
image. Reject blobs on the border of the image when you suspect that the
information about those blobs is incomplete.
Use imaqSizeFilter()to remove large or small particles that do not
interest you. You can also use the IMAQ_ERODE, IMAQ_OPEN, and
IMAQ_POPENmethods in imaqMorphology()to remove small particles.
Unlike imaqSizeFilter(), these three operations alter the size and
shape of the remaining blobs.
Use the IMAQ_HITMISSmethod of imaqMorphology()to locate
particular configurations of pixels, which you define with a structuring
element. Depending on the configuration of the structuring element, the
IMAQ_HITMISSmethod can locate single isolated pixels, cross-shape or
longitudinal patterns, right angles along the edges of particles, and other
user-specified shapes. For more information about structuring elements,
see Chapter 9, Binary Morphology, of the IMAQ Vision Concepts Manual.
© National Instruments Corporation
4-3
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 4
Blob Analysis
If you know enough about the shape features of the blobs you want to keep,
use imaqParticleFilter()to filter out particles that do not interest
you. If you do not have enough information about the particles you want to
keep at this point in your processing, use the particle measurement
functions to obtain this information before applying a particle filter. See the
Make Particle Measurements section for more information about the
measurement functions.
Separating Touching Blobs
Use imaqSeparation()or apply an erosion or an open operation with
imaqMorphology()to separate touching objects. The
imaqSeparation()function is an advanced function that separates blobs
without modifying their shapes. However, erosion and open functions alter
the shape of all the blobs.
Note A separation is a time-intensive operation compared to an erosion or open operation.
Consider using an erosion or open if speed is an issue with your application.
Improving Blob Shapes
Use imaqFillHoles()to fill holes in the blobs. Use
imaqMorphology()to perform a variety of operations on the blobs. You
can use the IMAQ_AUTOM, IMAQ_CLOSE, IMAQ_PCLOSE, IMAQ_OPEN, and
IMAQ_POPENmethods to smooth the boundaries of the blobs. Open and
proper open smooth the boundaries of the blob by removing small
isthmuses while close widens the isthmuses. Close and proper close fill
small holes in the blob. Auto-median removes isthmuses and fills holes.
For more information about these methods, see Chapter 9, Binary
Morphology, in the IMAQ Vision Concepts Manual.
Make Particle Measurements
After you create a binary image and improve it, you can make particle
measurements. With these measurements you can determine the location of
blobs and their shape features. Use the following functions to perform
particle measurements:
• imaqGetParticleInfo()—This function returns the number of
blobs in an image and a report containing the pixel area, real-world
rectangle to determine the location of the blob in the image. You can
also have this function return a report containing 16 of the most
IMAQ Vision for LabWindows/CVI User Manual
4-4
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 4
Blob Analysis
commonly used measurements, including the area, projection along
the x-axis and y-axis, and perimeter of each blob.
• imaqSelectParticles()—This function selects information about
Blobs that do not meet the criteria you set are filtered from the reports.
• imaqCalcCoeff()—This function uses the reports from
imaqGetParticleInfo()or imaqSelectParticles()to
calculate 50 particle measurements.
Table 4-1 lists all of the measurements that imaqCalcCoeff()returns.
Table 4-1. Particle Measurements
Measurement
Description
area of the particle in pixels
IMAQ_AREA
IMAQ_AREA_CALIBRATED
IMAQ_NUM_HOLES
area of the particle in user-defined units
number of holes within the particle
total area of the particle holes in pixels
area of the particle and its holes
IMAQ_AREA_OF_HOLES
IMAQ_TOTAL_AREA
IMAQ_IMAGE_AREA
IMAQ_PARTICLE_TO_IMAGE
area of the entire image in real-world units
ratio, expressed as a percent, of the surface area of a
particle in relation to the image area
IMAQ_PARTICLE_TO_TOTAL
ratio, expressed as a percent, of the surface area of a
particle in relation to the total area of the particle
IMAQ_CENTER_MASS_X
IMAQ_CENTER_MASS_Y
IMAQ_LEFT_COLUMN
IMAQ_TOP_ROW
x-coordinate of the center of mass
y-coordinate of the center of mass
left x-coordinate of the bounding rectangle
top y-coordinate of the bounding rectangle
right x-coordinate of the bounding rectangle
bottom y-coordinate of bounding rectangle
width of bounding rectangle in user-defined units
height of bounding rectangle in user-defined units
length of longest horizontal line segment in a particle
IMAQ_RIGHT_COLUMN
IMAQ_BOTTOM_ROW
IMAQ_WIDTH
IMAQ_HEIGHT
IMAQ_MAX_SEGMENT_LENGTH
© National Instruments Corporation
4-5
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 4
Blob Analysis
Table 4-1. Particle Measurements (Continued)
Measurement
Description
IMAQ_MAX_SEGMENT_LEFT_COLUMN
leftmost x-coordinate of longest horizontal line segment
in a particle
IMAQ_MAX_SEGMENT_TOP_ROW
IMAQ_PERIMETER
y-coordinate of longest horizontal line segment
length of the outer contour of the particle in user-defined
units
IMAQ_PERIMETER_OF_HOLES
IMAQ_SIGMA_X
perimeter of all holes in user-defined units
sum of the x-coordinates for each pixel of the particle
sum of the y-coordinates for each pixel of the particle
IMAQ_SIGMA_Y
IMAQ_SIGMA_XX
sum of the squared x-coordinates for each pixel of the
particle
IMAQ_SIGMA_YY
IMAQ_SIGMA_XY
sum of the squared y-coordinates for each pixel of the
particle
sum of the product of the x-coordinate and y-coordinate
for each pixel of the particle
IMAQ_PROJ_X
sum of the vertical segments in a particle
sum of the horizontal segments in a particle
inertia matrix coefficient in XX
IMAQ_PROJ_Y
IMAQ_INERTIA_XX
IMAQ_INERTIA_YY
IMAQ_INERTIA_XY
IMAQ_MEAN_H
inertia matrix coefficient in YY
inertia matrix coefficient in XY
mean length of horizontal segments
mean length of vertical segments
IMAQ_MEAN_V
IMAQ_MAX_INTERCEPT
length of the longest segment of the convex hull of the
particle
IMAQ_MEAN_INTERCEPT
mean length of the chords in a particle perpendicular to
its max intercept
IMAQ_ORIENTATION
direction of the major axis of a particle
IMAQ_EQUIV_ELLIPSE_MINOR
total length of the minor axis of the ellipse having the
same area as the particle and a major axis equal to half
the max intercept
IMAQ Vision for LabWindows/CVI User Manual
4-6
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 4
Blob Analysis
Table 4-1. Particle Measurements (Continued)
Description
Measurement
IMAQ_ELLIPSE_MAJOR
total length of the major axis of the ellipse having the
same area and perimeter as the particle in user-defined
units
IMAQ_ELLIPSE_MINOR
IMAQ_ELLIPSE_RATIO
total length of the minor axis of the ellipse having the
same area and perimeter as the particle in user-defined
units
fraction of the length of the major axis to the length of
the minor axis of the ellipse having the same area and
perimeter as the particle in user-defined units
IMAQ_RECT_LONG_SIDE
IMAQ_RECT_SHORT_SIDE
length of the long side of a rectangle having the same
area and perimeter as the particle in user-defined units
length of the short side of a rectangle having the same
area and perimeter as the particle in user-defined units
IMAQ_RECT_RATIO
IMAQ_ELONGATION
IMAQ_COMPACTNESS
IMAQ_HEYWOOD
ratio of rectangle long side to rectangle short side
max intercept/mean perpendicular intercept
particle area/(height × width)
particle perimeter/perimeter of circle having same area
as the particle
IMAQ_TYPE_FACTOR
complex factor relating the surface area to the moment of
inertia
IMAQ_HYDRAULIC
particle area/particle perimeter
IMAQ_WADDLE_DISK
diameter of the disk having the same area as the particle
in user-defined units
IMAQ_DIAGONAL
diagonal of an equivalent rectangle in user-defined units
Convert Pixel Coordinates to Real-World Coordinates
If you need to find the location of the center of mass or the bounding
rectangle of the blobs in real-world units, use
imaqTransformPixelToRealWorld().
© National Instruments Corporation
4-7
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
5
Machine Vision
This chapter describes how to perform many common machine vision
inspection tasks. The most common inspection tasks are detecting the
presence or absence of parts in an image and measuring the dimensions
of parts to see if they meet specifications.
Measurements are based on characteristic features of the object represented
in the image. Image processing algorithms traditionally classify the type
of information contained in an image as edges, surfaces and textures, or
patterns. Different types of machine vision algorithms leverage and extract
one or more types of information.
Edge detectors and derivative techniques—such as rakes, concentric rakes,
and spokes—use edges represented in the image. They locate with high
accuracy the position of the edge of an object in the image. For example,
you can use the edge location to measure the width of the part (a technique
called clamping). You can combine multiple edge locations to compute
intersection points, projections, circles, or ellipse fits.
Pattern matching algorithms use edges and patterns. Pattern matching can
locate with very high accuracy the position of fiducials or characteristic
features of the part under inspection. Those locations can then be combined
to compute lengths, angles, and other object measurements.
The robustness of your measurement relies on the stability of your image
acquisition conditions. Sensor resolution, lighting, optics, vibration
control, part fixture, and general environment are key components of the
imaging setup. All the elements of the image acquisition chain directly
affect the accuracy of the measurements.
© National Instruments Corporation
5-1
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Figure 5-1 illustrates the basic steps involved in performing machine
vision. Diagram items enclosed with dashed lines are optional steps.
Locate Objects to Inspect
Set Search Areas
Find Measurement Points
Convert Pixel Coordinates to
Real-World Coordinates
Make Measurements
Display Results
Figure 5-1. Steps to Performing Machine Vision
Locate Objects to Inspect
In a typical machine vision application, you extract measurements from
parts of the object you are interested in must always appear inside the
regions of interest you define.
If the object under inspection is always at the same location and orientation
in the images you need to process, defining regions of interest is simple.
See the Set Search Areas section for information about selecting a region
of interest.
Often, the object under inspection appears rotated or shifted in the image
you need to process with respect to the reference image in which you
located the object. When this occurs, the regions of interest (ROIs) need to
shift and rotate with the parts of the object in which you are interested. In
order for the ROIs to move with the object, you need to define a reference
coordinate system relative to the object in the reference image. During the
measurement process, the coordinate system moves with the object when it
IMAQ Vision for LabWindows/CVI User Manual
5-2
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
appears shifted and rotated in the image you need to process. This
coordinate system is referred to as the measurement coordinate system.
The measurement methods automatically move the ROIs to the correct
position using the position of the measurement coordinate system with
respect to the reference coordinate system. For information about
coordinate systems, see Chapter 13, Dimensional Measurements, of
the IMAQ Vision Concepts Manual.
You can build a coordinate transform using edge detection or pattern
matching. The output of the edge detection and pattern matching functions
that build a coordinate system are the origin, angle, and axes direction of
the coordinate system. Some machine vision functions take this output and
adjust the regions of inspection automatically. You can also use these
outputs to move the regions of inspection relative to the object
programmatically.
Using Edge Detection to Build a Coordinate Transform
You can build a coordinate transform using two edge detection techniques.
Use imaqFindTransformRect()to define a coordinate system using
one rectangular region. Use imaqFindTransformRects()to define a
coordinate system using two independent rectangular regions. Follow the
steps below to build a coordinate transform using edge detection:
1. Specify one or two rectangular regions.
a. If you use imaqFindTransformRect(), specify one rectangular
ROI that includes part of two straight, nonparallel boundaries of
the object, as shown in Figure 5-2. This rectangular region must
be large enough to include these boundaries in all the images you
want to inspect.
© National Instruments Corporation
5-3
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
1
1
2
4
2
3
3
4
a.
b.
1
2
Search Area for the Coordinate System
Object Edges
3
4
Origin of the Coordinate System
Measurement Area
Figure 5-2. Coordinate Systems of a Reference Image and Inspection Image
b. If you use imaqFindTransformRects(), specify two
rectangles, each containing one separate, straight boundary of the
object, as shown in Figure 5-3. The boundaries cannot be parallel.
The regions must be large enough to include the boundaries in all
the images you want to inspect.
IMAQ Vision for LabWindows/CVI User Manual
5-4
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
4
2
4
2
3
3
1
1
b.
a.
1
2
Primary Search Area
Secondary Search Area
3
4
Origin of the Coordinate System
Measurement Area
Figure 5-3. Locating Coordinate System Axes with Two Search Areas
2. Use the options parameter to choose the options you need to locate the
edges on the object, the coordinate system axis direction, and the
results that you want to overlay onto the image. Set the options
parameter to NULL to use the default options.
3. Choose the mode for the function. To build a coordinate transform for
the first time, set mode to IMAQ_FIND_REFERENCE. To update the
coordinate transform in subsequent images, set this mode to
IMAQ_UPDATE_TRANSFORM.
Using Pattern Matching to Build a Coordinate Transform
You can build a coordinate transform using pattern matching. Use
imaqFindTransformPattern()to define a coordinate system based on
the location of a reference feature. Use this technique when the object under
inspection does not have straight, distinct edges. Follow the steps below to
build a coordinate reference system using pattern matching:
rotation-invariant pattern matching.
© National Instruments Corporation
5-5
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
1. Define a template that represents the part of the object that you want
to use as a reference feature. For more information about defining a
template, see the Find Measurement Points section.
2. Define a rectangular search area in which you expect to find the
template.
3. Use the options parameter to select your options for finding the pattern
and the results that you want to overlay onto the image. When setting
the Modeelement, select IMAQ_MATCH_ROTATION_INVARIANTwhen
you expect your template to appear rotated in the inspection images.
Otherwise, select IMAQ_MATCH_SHIFT_INVARIANT. Set the options
parameter to NULL to use the default options.
4. Choose the mode for the function. To build a coordinate transform for
the first time, set mode to IMAQ_FIND_REFERENCE. To update the
coordinate system in subsequent images, set mode to
IMAQ_UPDATE_TRANSFORM.
IMAQ Vision for LabWindows/CVI User Manual
5-6
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Choosing a Method to Build the Coordinate Transform
The following flowchart guides you through choosing the best method for
building a coordinate transform for your application.
Start
Object positioning
accuracy better
No
than 65 degrees.
Yes
The object under
inspection has a straight,
distinct edge (main axis).
No
Yes
The object contains a
second distinct edge not parallel
to the main axis in the same
search area.
No
The object contains
a second distinct edge not
parallel to the main axis in a
separate search area.
No
Yes
Build a
coordinate transformation
based on edge detection
using a single search area.
Object positioning
accuracy better
than 5 degrees.
No
Yes
Build a coordinate
transformation based on
edge detection using two
distinct search areas.
Yes
Build a coordinate
transformation based on
pattern matching
Build a coordinate
transformation based on
pattern matching
rotation invariant strategy.
shift invariant strategy.
End
Figure 5-4. Building a Coordinate Transform
© National Instruments Corporation
5-7
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Set Search Areas
Select regions of interest (ROIs) in your images to limit the areas in which
you perform your processing and inspection. You can define ROIs
interactively or programmatically.
Interactively Defining Regions
Follow these steps to interactively define an ROI:
1. Use imaqConstructROI()to display an image and the tools palette
in a window.
2. Select an ROI tool from the tools palette.
3. Draw an ROI on your image. Resize and reposition the ROI until it
specifies the area you want to process.
4. Click OK to output a descriptor of the region you selected. You can
input the ROI descriptor into many analysis and processing functions.
You can also use imaqSelectRect()and imaqSelectAnnulus()to
define regions of interest. Follow these steps to use these functions:
1. Call the function to display an image in a window. Only the tools
specific to that function are available for you to use.
2. Select an ROI tool from the tools palette.
3. Draw an ROI on your image. Resize or reposition the ROI until it
specifies the area you want to process.
Click OK to output a simple description of the ROI. You can use this
description as an input for the following functions:
ROI Selection Function
Measurement Function
imaqFindPattern()
imaqSelectRect()
imaqClampMax()
imaqClampMin()
imaqFindEdge()
imaqFindCircularEdge()
imaqFindConcentricEdge()
imaqSelectAnnulus()
IMAQ Vision for LabWindows/CVI User Manual
5-8
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Programmatically Defining Regions
When you have an automated application, you need to define regions of
interest programmatically. You can programmatically define regions in
two ways:
•
•
Specify the contours of the ROI.
Specify individual structures by providing basic parameters that
describe the region you want to define. You can specify a rotated
rectangle by providing the coordinates of the center, the width, the
the coordinates of the center, inner radius, outer radius, start angle, and
end angle. You can specify a point by setting its x-coordinates and
y-coordinates. You can specify a line by setting the coordinates of the
start and end points.
See Chapter 3, Grayscale and Color Measurements, for more information
about defining regions of interest.
Find Measurement Points
After you set regions of inspection, locate points within those regions on
which you can base measurements. You can locate measurement points
using edge detection, pattern matching, color pattern matching, and color
location.
Finding Features Using Edge Detection
Use the edge detection tools to identify and locate sharp discontinuities
in an image. Discontinuities typically represent abrupt changes in pixel
intensity values, which characterize the boundaries of objects.
Finding Lines or Circles
If you want to find points along the edge of an object and find
a line describing the edge, use imaqFindEdge()and
imaqFindConcentricEdges(). The imaqFindEdge()function finds
edges based on rectangular search areas, as shown in Figure 5-5. The
imaqFindConcentricEdge()function finds edges based on annular
search areas.
© National Instruments Corporation
5-9
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
If you want to find points along a circular edge and find the circle that best
fits the edge, as shown in Figure 5-6, use imaqFindCircularEdge().
1
4
3
2
1
2
Annular Search Region
Search Lines
3
4
Detected Edge Points
Circle Fit To Edge Points
Figure 5-6. Finding a Circular Feature
These functions locate the intersection points between a set of search
lines within the search region and the edge of an object. You can specify the
search region using imaqSelectRect()or imaqSelectAnnulus().
Specify the separation between the lines that the functions use to detect
edges. The functions determine the intersection points based on their
contrast, width, and steepness. The software calculates a best-fit line with
outliers rejected or a best-fit circle through the points it found. The
functions return the coordinates of the edges found.
Finding Edge Points Along One Search Contour
Use imaqSimpleEdge()or imaqEdgeTool()to find edge points along
a contour. Using imaqSimpleEdge(), you can find the first edge, last
edge, or all edges along the contour. Use imaqSimpleEdge()when your
image contains little noise and the object and background are clearly
differentiated. Otherwise, use imaqEdgeTool().
© National Instruments Corporation
5-11
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
These functions require you to input the coordinates of the points along the
search contour. Use imaqROIProfile()to obtain the coordinates along
the edge of each contour in an ROI. If you have a straight line, use
imaqGetPointsOnLine()to obtain the points along the line instead of
using an ROI.
These functions determine the edge points based on their contrast and
slope. You can specify whether you want to find the edge points using
subpixel accuracy.
Finding Edge Points Along Multiple Search Contours
Use imaqRake(), imaqSpoke(), and imaqConcentricRake()to find
edge points along multiple search contours. These functions behave like
imaqEdgeTool()behaves, but they find edges on multiple contours. Pass
in an ROI to define the search region for these functions.
The imaqRake()function works on a rectangular search region. The
search lines are drawn parallel to the orientation of the rectangle. Control
the number of search lines in the region by specifying the distance, in
pixels, between each line. Specify the search direction as left to right or
right to left for a horizontally oriented rectangle. Specify the search
direction as top to bottom or bottom to top for a vertically oriented
rectangle.
The imaqSpoke()function works on an annular search region, scanning
the search lines that are drawn from the center of the region to the outer
boundary and that fall within the search area. Control the number of lines
in the region by specifying the angle, in degrees, between each line. Specify
the search direction as either going from the center outward or from the
outer boundary to the center.
The imaqConcentricRake()function works on an annular search
region. The concentric rake is an adaptation of the Rake to an annular
region. IMAQ Vision performs edge detection along search lines that occur
in the search region and that are concentric to the outer circular boundary.
Control the number of concentric search lines that are used for the edge
detection by specifying the radial distance between the concentric lines in
pixels. Specify the direction of the search as either clockwise or
counterclockwise.
IMAQ Vision for LabWindows/CVI User Manual
5-12
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Finding Points Using Pattern Matching
The pattern matching algorithms in IMAQ Vision measure the similarity
between an idealized representation of a feature, called a template, and the
feature that may be present in an image. A feature is defined as a specific
pattern of pixels in an image. Pattern matching returns the location of the
center of the template and the template orientation. Follow these
generalized steps to find features in an image using pattern matching:
1. Define a reference or fiducial pattern in the form of a template image.
2. Use the reference pattern to train the pattern matching algorithm with
imaqLearnPattern().
3. Define an image or an area of an image as the search area. A small
search area reduces the time to find the features.
4. Set the tolerances and parameters to specify how the algorithm
operates at run time using the options parameter of
imaqMatchPattern().
5. Test the search algorithm on test images using
imaqMatchPattern().
6. Verify the results using a ranking method.
Defining and Create Good Template Images
The selection of a good template image plays a critical part in obtaining
good results. Because the template image represents the pattern that you
want to find, make sure that all the important and unique characteristics of
the pattern are well defined in the image.
Several factors are critical in creating a template image. These critical
factors include symmetry, feature detail, positional information, and
background information.
Symmetry
A rotationally symmetric template is less sensitive to changes in rotation
than one that is rotationally asymmetric. A rotationally symmetric template
provides good positioning information but no orientation information.
© National Instruments Corporation
5-13
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Rotationally
Symmetric
Rotationally
Asymmetric
Figure 5-7. Symmetry
Feature detail
A template with relatively coarse features is less sensitive to variations in
size and rotation than a model with fine features. However, the model must
contain enough detail to identify it.
Good
Ambiguous
Feature Detail
Feature Detail
Figure 5-8. Feature Detail
Positional information
A template with strong edges in both the x and y directions is easier to
locate.
Good Positional
Information in x and y
Insufficient Positional
Information in y
Figure 5-9. Positional Information
IMAQ Vision for LabWindows/CVI User Manual
5-14
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Background information
Unique background information in a template improves search
performance and accuracy.
Pattern with Insufficient
Background Information
Pattern with Sufficient
Background Information
Figure 5-10. Background Information
Training the Pattern Matching Algorithm
After you create a good template image, the pattern matching
algorithm has to learn the important features of the template. Use
imaqLearnPattern()to learn the template. The learning process
depends on the type of matching that you expect to perform. If you do not
expect the instance of the template in the image to rotate or change its size,
then the pattern matching algorithm has to learn only those features from
the template that are necessary for shift-invariant matching. However, if
you want to match the template at any orientation, use rotation-invariant
matching. Use the learningMode parameter of imaqLearnPattern()to
specify which type of learning mode to use.
The learning process is usually time intensive because the algorithm
attempts to find the optimum features of the template for the particular
matching process. The learning mode you choose also affects the speed of
the learning process. Learning the template for shift-invariant matching is
faster than learning for rotation-invariant matching. You can also save time
by training the pattern matching algorithm offline and then saving the
template image with imaqWriteVisionFile().
Defining a Search Area
Two equally important factors define the success of a pattern matching
algorithm: accuracy and speed. You can define a search area to reduce
ambiguity in the search process. For example, if your image has multiple
instances of a pattern and only one of them is required for the inspection
task, the presence of additional instances of the pattern can produce
© National Instruments Corporation
5-15
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
incorrect results. To avoid this, reduce the search area so that only the
desired pattern lies within the search area.
The time required to locate a pattern in an image depends on both the
template size and the search area. By reducing the search area or increasing
the template size, you can reduce the required search time.
area. For example, in a typical component placement application, each
printed circuit board (PCB) being tested may not be placed in the same
images can move and rotate within a known range of values, as illustrated
in Figure 5-11. Figure 5-11a shows the template used to locate the PCB in
the image. Figure 5-11b shows an image containing a PCB with a fiducial
you want to locate. Notice the search area around the fiducial. If you know
before the matching process begins that the PCB can shift or rotate in the
image within a fixed range (as shown in Figure 5-11c and Figure 5-11d,
respectively), then you can limit the search for the fiducial to a small region
of the image.
a.
b.
c.
d.
Figure 5-11. Selecting a Search Area for Grayscale Pattern Matching
Every pattern matching algorithm makes assumptions about the images
and pattern matching parameters used in machine vision applications.
These assumptions work for a high percentage of the applications.
IMAQ Vision for LabWindows/CVI User Manual
5-16
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
However, there may be applications in which the assumptions used in the
algorithm are not optimal. Knowing your particular application and the
images you want to process is useful in selecting the pattern matching
parameters. The following are parameters the influence the IMAQ Vision
pattern matching algorithm.
Match Mode
You can set the match mode to control how the pattern matching algorithm
treats the template at different orientations. If you expect the orientation of
valid matches to vary less than 5° from the template, set the modeelement
of the options parameter to IMAQ_MATCH_SHIFT_INVARIANT. Otherwise,
set the mode element to IMAQ_MATCH_ROTATION_INVARIANT.
Shift-invariant matching is faster than rotation-invariant matching.
Minimum Contrast
You can set the minimum contrast to potentially increase the pattern
matching algorithm’s speed. Contrast is the difference between the smallest
and largest pixel values in a region. The pattern matching algorithm ignores
all image regions where contrast values fall beneath a set minimum contrast
value. If the search image has high contrast but contains some low contrast
regions, you can set a high minimum contrast value. By using a high
minimum contrast value, you exclude all areas in the image with low
contrast, significantly reducing the region in which the pattern matching
algorithm must search. If the search image has low contrast throughout, set
a low minimum contrast parameter to ensure that the pattern matching
algorithm looks for the template in all regions of the image. Use the
minContrastelement of the imaqMatchPattern()options parameter
to set the minimum contrast.
Rotation Angle Ranges
For matching objects that may rotate in the image, the pattern matching
algorithm, by default, allows any orientation between 0° to 360°. If the
pattern rotation in your application is restricted to a certain range (for
example, from –15° to 15°), you can provide this information to the pattern
matching algorithm by setting the angleRangeselement of the
imaqMatchPattern()options parameter. This information improves
your search time because the pattern matching algorithm looks for the
pattern at fewer angles.
© National Instruments Corporation
5-17
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Testing the Search Algorithm on Test Images
To determine if your selected template or reference pattern is appropriate
for your machine vision application, test the template on a few test images
by using imaqMatchPattern(). These test images should reflect the
images generated by your machine vision application during true operating
conditions. If the pattern matching algorithm locates the reference pattern
in all cases, you have selected a good template. Otherwise, refine the
current template, or select a better template until both training and testing
are successful.
Using a Ranking Method to Verify Results
The manner in which you interpret the pattern matching algorithm depends
on your application. For typical alignment applications, such as finding a
fiducial on a wafer, the most important information is the position and
location of the best match. Use the positionand cornerelements of the
Pattern Matchstructure to get the position and the bounding rectangle
of a match.
In inspection applications, such as optical character verification (OCV), the
score of the best match is more useful. The score of a match returned by the
pattern matching algorithm is an indicator of the closeness between the
original pattern and the match found in the image. A high score indicates a
very close match, while a low score indicates a poor match. The score can
be used as a gauge to determine whether a printed character is acceptable.
Use the scoreelement of the Pattern Matchstructure to get the score
corresponding to a match.
Finding Points Using Color Pattern Matching
Color pattern matching algorithms provide a quick way to locate objects
when color is present. Use color pattern matching if:
•
The object you want to locate has color information that is very
different from the background, and you want to find a very precise
location of the object in the image.
•
The object to locate has grayscale properties that are very difficult to
characterize or that are very similar to other objects in the search
image. In such cases, grayscale pattern matching can give inaccurate
results. If the object has color information that differentiates it from the
other objects in the scene, color provides the machine vision software
with the additional information to locate the object.
IMAQ Vision for LabWindows/CVI User Manual
5-18
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Color pattern matching returns the location of the center of the template and
the template orientation. Follow these general steps to find features in an
image using color pattern matching:
1. Define a reference or fiducial pattern in the form of a template image.
2. Use the reference pattern to train the color pattern matching algorithm
with imaqLearnColorPattern().
3. Define an image or an area of an image as the search area. A small
search area reduces the time to find the features.
4. Set the featureModeelement of the imaqMatchColorPattern()
options parameter to IMAQ_COLOR_AND_SHAPE_FEATURES.
5. Set the tolerances and parameters to specify how the algorithm
operates at run time using the options parameter of
imaqMatchColorPattern().
6. Test the search algorithm on test images using
imaqMatchColorPattern().
7. Verify the results using a ranking method.
Defining and Creating Good Color Template Images
The selection of a good template image plays a critical part in obtaining
accurate results with the color pattern matching algorithm. Because the
template image represents the color and the pattern that you want to find,
make sure that all the important and unique characteristics of the pattern are
well defined in the image.
Several factors are critical in creating a template image. These critical
factors include color information, symmetry, feature detail, positional
information, and background information.
Color Information
A template with colors that are unique to the pattern provides better results
than a template that contains many colors, especially colors found in the
background or other objects in the image.
Symmetry
A rotationally symmetric template in the luminance plane is less sensitive
to changes in rotation than one that is rotationally asymmetric.
© National Instruments Corporation
5-19
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Feature Detail
A template with relatively coarse features is less sensitive to variations in
size and rotation than a model with fine features. However, the model must
contain enough detail to identify it.
Positional Information
A template with strong edges in both the x and y directions is easier to
locate.
Background Information
Unique background information in a template improves search
performance and accuracy during the grayscale pattern matching phase.
This requirement could conflict with the “color information” requirement
because background colors may not be desirable during the color location
phase. Avoid this problem by choosing a template with sufficient
background information for grayscale pattern matching while specifying
the exclusion of the background color during the color location phase.
Refer to the Training the Color Pattern Matching Algorithm section for
more information about how to ignore colors.
Training the Color Pattern Matching Algorithm
After you have created a good template image, the color pattern
matching algorithm learns the important features of the template. Use
imaqLearnColorPattern()to learn the template.The learning process
depends on the type of matching that you expect to perform. By default,
the color pattern matching algorithm learns only those features from the
template that are necessary for shift-invariant matching. However, if you
want to match the template at any orientation, the learning process must
consider the possibility of arbitrary orientations. Use the learnMode
element of the imaqLearnColorPattern()options parameter to
specify which type of learning mode to use.
Exclude colors in the template that you are not interested in using during
the search phase. Typically you should ignore colors that either belong to
the background of the object or are not unique to the template, reducing the
potential for incorrect matches during the color location phase. You can
ignore certain predefined colors using the ignoreModeelement of the
options parameter. To ignore other colors, first learn the colors to ignore
options parameter to the resulting ColorInformationstructure from
imaqLearnColor().
IMAQ Vision for LabWindows/CVI User Manual
5-20
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
The training or learning process is time-intensive because the algorithm
attempts to find optimal features of the template for the particular matching
process. However, you can train the pattern matching algorithm offline, and
save the template image using imaqWriteVisionFile().
Defining a Search Area
Two equally important factors define the success of a color pattern
matching algorithm—accuracy and speed. You can define a search area to
reduce ambiguity in the search process. For example, if your image has
multiple instances of a pattern and only one instance is required for the
inspection task, the presence of additional instances of the pattern can
the desired pattern lies within the search area. For example, in the fuse box
inspection example use the location of the fuses to be inspected to define
the search area. Because the inspected fuse box may not be in the exact
location or have the same orientation in the image as the previous one, the
search area you define should be large enough to accommodate these
variations in the position of the box. Figure 5-12 shows how search areas
can be selected for different objects.
© National Instruments Corporation
5-21
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
1
2
1
Search Area for 20 Amp Fuses
2
Search Area for 25 Amp Fuses
Figure 5-12. Selecting a Search Area for Color Pattern Matching
The time required to locate a pattern in an image depends on both the
template size and the search area. By reducing the search area or increasing
the template size, you can reduce the required search time.
Setting Matching Parameters and Tolerances
Every color pattern matching algorithm makes assumptions about the
images and color pattern matching parameters used in machine vision
applications. These assumptions work for a high percentage of the
applications.
In some applications, the assumptions used in the algorithm are not
optimal. In such cases, you must modify the color pattern matching
parameters. Knowing your particular application and the images you want
to process is useful in selecting the pattern matching parameters. Use the
options parameter of imaqMatchColorPattern()to set these elements.
IMAQ Vision for LabWindows/CVI User Manual
5-22
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
The following are some elements in the IMAQ Vision pattern matching
algorithm and how they influence pattern matching: color sensitivity,
search strategy, color score weight, ignore background colors, minimum
contrast, and rotation angle ranges.
Color Sensitivity
Use the sensitivityelement to control the granularity of the color
information in the template image. If the background and objects in the
image contain colors that are very close to colors in the template image, use
a higher color sensitivity setting. A higher sensitivity setting distinguishes
colors with very close hue values. Three color sensitivity settings are
available in IMAQ Vision: IMAQ_SENSITIVITY_LOW,
IMAQ_SENSITIVITY_MED, and IMAQ_SENSITIVITY_HIGH. Use the
default Low setting if the colors in the template are very different from the
colors in the background or other objects that you are not interested in.
Increase the color sensitivity settings as the color differences decrease. For
more information on color sensitivity, see Chapter 14, Color Inspection, of
the IMAQ Vision Concepts Manual.
Search Strategy
Use the strategyelement to optimize the speed of the color pattern
matching algorithm. The search strategy controls the step size,
sub-sampling factor, percentage of color information used from the
template.
Choose from four different strategies:
• IMAQ_VERY_AGGRESSIVE—Uses the largest step size, the most
sub-sampling and only the dominant color from the template to search
for the template. Use this strategy when the color in the template is
almost uniform, the template is well contrasted from the background
and there is a good amount of separation between different occurrences
of the template in the image. This strategy is the fastest way to find
templates in an image.
• IMAQ_AGGRESSIVE—Uses a large step size, a lot of sub-sampling,
and little of the color information from the template.
• IMAQ_BALANCED—Uses values in between the IMAQ_AGGRESSIVE
and IMAQ_CONSERVATIVEstrategies.
• IMAQ_CONSERVATIVE—Uses a very small step size, a sub-sampling
factor of two, and all the color information present in the template.
This strategy is the most reliable method to look for a template in any
image at potentially reduced speed.
© National Instruments Corporation
5-23
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Note Use the IMAQ_CONSERVATIVEstrategy if you have multiple targets located very
close to each other in the image.
Decide on the best strategy by experimenting with the different options.
Color Score Weight
When you search for a template using both color and shape information, the
color and shape scores generated during the match process are combined to
generate the final color pattern matching score. The color score weight
determines the contribution of the color score to the final color pattern
matching score. If the template’s color information is superior to its shape
information, set the weight higher. For example, if you set colorWeight
to 1000, the algorithm finds each match by using both color and shape
information and then ranks the matches based entirely on their color scores.
If you set colorWeightto 0, the matches are ranked based entirely on
their shape scores.
Minimum Contrast
Use the minContrastelement to increase the color pattern matching
algorithm’s speed. The color pattern matching algorithm ignores all image
regions where grayscale contrast values fall beneath a set minimum
contrast value. See the Setting Matching Parameters and Tolerances
section of this chapter for more information about minimum contrast.
Rotation Angle Ranges
If you know that the pattern rotation is restricted to a certain range (for
example, between –15° to 15°), provide this restriction information to
the pattern matching algorithm by setting the angleRangeselement. This
information improves your search time because the color pattern matching
algorithm looks for the pattern at fewer angles. See Chapter 12, Pattern
Matching, in the IMAQ Vision Concepts Manual for more information on
pattern matching.
Testing the Search Algorithm on Test Images
To determine if your selected template or reference pattern is appropriate
for your machine vision application, test the template on a few test images
by using imaqMatchColorPattern(). These test images should reflect
the images generated by your machine vision application during true
operating conditions. If the pattern matching algorithm locates the
IMAQ Vision for LabWindows/CVI User Manual
5-24
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
reference pattern in all cases, you have selected a good template.
Otherwise, refine the current template, or select a better template until both
training and testing are successful.
Finding Points Using Color Location
Color location algorithms provide a quick way to locate regions in an image
with specific colors.
Use color location when your application:
•
Requires the location and the number of regions in an image with their
specific color information
•
Relies on the cumulative color information in the region, instead of the
color arrangement in the region
•
•
•
Does not require the orientation of the region
Does not always require the location with sub-pixel accuracy
Does not require shape information for the region
Follow these general steps to find features in an image using color location:
1. Define a reference pattern in the form of a template image.
2. Use the reference pattern to train the color location algorithm with
imaqLearnColorPattern().
3. Define an image or an area of an image as the search area. A small
search area reduces the time to find the features.
4. Set the featureModeelement of the imaqMatchColorPattern()
options parameter to IMAQ_COLOR_FEATURES.
5. Set the tolerances and parameters to specify how the algorithm
operates at run time using the options parameter of
imaqMatchColorPattern().
6. Test the color location algorithm on test images using
imaqMatchColorPattern().
7. Verify the results using a ranking method.
You can save the template image using imaqWriteVisionFile().
© National Instruments Corporation
5-25
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Convert Pixel Coordinates to Real-World Coordinates
The measurement points you located with edge detection and pattern
matching are in pixel coordinates. If you need to make measurements using
real-world units, use imaqTransformPixelToRealWorld()to convert
the pixel coordinates into real-world units.
Make Measurements
You can make different types of measurements either directly from the
image or from points that you detect in the image.
Distance Measurements
Use the following functions to make distance measurements for your
inspection application.
Clamp functions measure the separation between two edges in a
rectangular search region. First, clamp functions detect points along the
two edges using the rake function. Then, they compute the distance
between the points detected on the edges along each search line of the rake
and return the largest or smallest distance. The imaqSelectRect()
function generates a valid input search region for these functions. You also
need to specify the parameters for edge detection and the separation
between the search lines that you want to use within the search region to
find the edges. These functions work directly on the image under
inspection, and they output the coordinates of all the edge points that they
find. The following list describes the available clamp functions:
• imaqClampMax()—Measures the largest separation between
two edges in a rectangular search region.
• imaqClampMin()—Finds the smallest separation between two edges.
Use imaqGetDistance()to compute the distances between two points,
such as consecutive pairs of points in an array of points. You can obtain
these points from the image using any one of the feature detection methods
described in the Find Measurement Points section of this chapter.
IMAQ Vision for LabWindows/CVI User Manual
5-26
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Analytic Geometry Measurements
Use the following functions to make geometrical measurements from the
points you detect in the image:
• imaqFitLine()—Fits a line to a set of points and computes the
equation of the line.
• imaqFitCircle()—Fits a circle to a set of at least three points and
computes its area, perimeter and radius.
• imaqFitEllipse()—Fits an ellipse to a set of at least six points and
computes its area, perimeter, and the lengths of its major and minor
axis.
• imaqGetIntersection()—Finds the intersection point of two lines
specified by their start and end points.
• imaqGetAngle()—Finds the smaller angle between two lines.
• imaqGetPerpendicularLine()—Finds the perpendicular line
from a point to a line and computes the perpendicular distance between
the point and the line.
• imaqGetBisectingLine()—Finds the line that bisects the angle
formed by two lines.
• imaqGetMidLine()—Finds the line that is midway between a point
and a line and is parallel to the line.
• imaqGetPolygonArea()—Calculates the area of a polygon
specified by its vertex points.
Instrument Reader Measurements
You can make measurements based on the values obtained by meter, LCD,
and barcode readers.
Use imaqGetMeterArc()to calibrate a meter or gauge that you want to
read. The imaqGetMeterArc()function calibrates the meter using one of
two modes. The IMAQ_METER_ARC_ROImode uses the initial position and
the full-scale position of the needle. When using this mode, the function
calculates the position of the base of the needle and the arc traced by the tip
of the needle. The IMAQ_METER_ARC_POINTSmode calibrates the meter
using three points on the meter: the base of the needle, the tip of the needle
at its initial position, and the tip of the needle at its full-scale position.
When using this mode, the function calculates the position of the points
read the position of the needle using the base of the needle and the array of
points on the arc traced by the tip of the needle.
© National Instruments Corporation
5-27
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
Use imaqFindLCDSegments()to calculate the regions of interest around
each digit in an LCD or LED. To find the area of each digit, all the segments
of the indicator must be activated. Use imaqReadLCD()to read multiple
digits of an LCD or LED.
Use imaqReadBarcode()to read values encoded in 1D barcodes. First,
specify a region of interest that encloses the barcode information, and
specify the type of barcode. Then, read the barcode.
Display Results
You can display the results obtained at various stages of you inspection
process on the window that displays your inspection image. You can do
this by overlaying information on an image. The software attaches the
information that you want to overlay to the image, but it does not modify
the image. The overlay appears every time you display the image in an
external window.
Use the following functions to overlay search regions, inspection results,
and other information, such as text and bitmaps.
• imaqOverlayPoints()—Overlays points on an image. Specify a
point by its x-coordinate and y-coordinate.
• imaqOverlayLine()—Overlays a line on an image. Specify a line
by its start and end points.
• imaqOverlayRect()—Overlays a rectangle on an image.
• imaqOverlayOval()—Overlays an oval or a circle on the image.
• imaqOverlayArc()—Overlays an arc on the image.
• imaqOverlayMetafile()—Overlays a metafile on the image.
• imaqOverlayText()—Overlays text on an image.
• imaqOverlayROI()—Overlays an ROI on an image.
• imaqOverlayClosedContour()—Overlays a closed contour on an
image.
• imaqOverlayOpenContour()—Overlays an open contour on an
image.
To use these functions, pass in the image on which you want to overlay
color of overlays by using the above functions.
IMAQ Vision for LabWindows/CVI User Manual
5-28
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 5
Machine Vision
You can configure the following processing functions to overlay different
types of information on the inspection image:
• imaqFindEdge()
• imaqFindCircularEdge()
• imaqFindConcentricEdge()
• imaqClampMax()
• imaqClampMin()
• imaqFindPattern()
• imaqCountObjects()
• imaqFindTransformRect()
• imaqFindTransformRects()
• imaqFindTransformPattern()
You can overlay the following information with all the above functions
except imaqFindPattern():
•
•
•
•
The search area input into the function.
The search lines used for edge detection.
The edges detected along the search lines
The result of the function.
With imaqFindPattern(), you can overlay the search area and the result.
Select the information you want to overlay by setting the element that
corresponds to the information type to TRUE in the options input
parameter.
Use imaqClearOverlay()to clear any previous overlay information
from the image. Use imaqWriteVisionFile()to save an image with
its overlay information to a file. You can read the information from the
file into an image using imaqReadVisionFile(). As with calibration
information, overlay information is removed from an image when the
image size or orientation changes.
© National Instruments Corporation
5-29
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
6
Calibration
This chapter describes how to calibrate your imaging system, save
calibration information, and attach calibration information to an image.
After you set up your imaging system, you may want to calibrate your
system. If your imaging setup is such that the camera axis is perpendicular
or nearly perpendicular to the object under inspection and your lens has no
distortion, use simple calibration. With simple calibration, you do not need
to learn a template. Instead, you define the distance between pixels in the
horizontal and vertical directions using real-world units.
If your camera axis is not perpendicular to the object under inspection or
your lens is distorted, use perspective and nonlinear distortion calibration
to calibrate your system.
Perspective and Nonlinear Distortion Calibration
Perspective errors and lens aberrations cause images to appear distorted.
This distortion misplaces information in an image, but it does not
necessarily destroy the information in the image. Calibrate your imaging
system if you need to compensate for perspective errors or nonlinear lens
distortion.
Follow these general steps to calibrate your imaging system:
1. Define a calibration template.
2. Define a reference coordinate system.
3. Learn the calibration information.
this chapter for more information. Depending on your needs, you can either
apply the calibration information to convert pixel coordinates to real-world
coordinates without correcting the image, or you can create a
distortion-free image by correcting the image for perspective errors and
lens aberrations. See Chapter 4, Blob Analysis, and Chapter 5, Machine
Vision, for more information about applying calibration information before
making measurements.
© National Instruments Corporation
6-1
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 6
Calibration
Defining a Calibration Template
You can define a calibration template by supplying an image of a grid or
providing a list of pixel coordinates and their corresponding real-world
coordinates. This section discusses the grid method in detail.
A calibration template is a user-defined grid of circular dots. As shown in
Figure 6-1, the grid has constant spacings in the x and y directions. You can
use any grid, but follow these guidelines for best results:
•
•
•
•
The displacement in the x and y directions should be equal (dx = dy).
The radius of the dots should be 6–10 pixels.
The center-to-center distance between dots should range from
18 to 32 pixels, as shown in Figure 6-1.
•
The minimum distance between the edges of the dots should be
6 pixels, as shown in Figure 6-1.
dx
1
dy
2
3
1
Center-to-Center Distance
2
Center of Grid Dots
3
Distance Between Dot Edges
Figure 6-1. Defining a Calibration Grid
Note You can use the calibration grid installed with IMAQ Vision at
Start»Programs»National Instruments»Vision»Documentation»Calibration Grid.
The dots have radii of 2 mm and center-to-center distances of 1 cm. Depending on your
printer, these measurements may change by a fraction of a millimeter. You can purchase
highly accurate calibration grids from optics suppliers, such as Edmund Industrial Optics.
Defining a Reference Coordinate System
To express measurements in real-world units, you need to define a
coordinate system in the image of the grid. Use the CoordinateSystem
structure to define a coordinate system by its origin, angle, and axis
direction.
IMAQ Vision for LabWindows/CVI User Manual
6-2
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 6
Calibration
The origin, expressed in pixels, defines the center of your coordinate
system. The angle specifies the orientation of your coordinate system
with respect to the angle of the topmost row of dots in the grid image.
The calibration procedure automatically determines the direction of the
horizontal axis in the real world. The vertical axis direction can either be
indirect, as shown in Figure 6-2a, or direct, as shown in Figure 6-2b.
X
Y
Y
X
a.
b.
Figure 6-2. Axis Direction in the Image Plane
If you do not specify a coordinate system, the calibration process defines a
default coordinate system. If you specify a grid for the calibration process,
the software defines the following default coordinate system, as shown in
Figure 6-3:
1. The origin is placed at the center of the left, topmost dot in the
calibration grid.
2. The angle is set to 0°. This aligns the x-axis with the first row of dots
in the grid, as shown in Figure 6-3b.
3. The axis direction is set to indirect. This aligns the y-axis to the first
column of the dots in the grid, as shown in Figure 6-3b.
© National Instruments Corporation
6-3
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 6
Calibration
1
2
x
y
a.
Origin of a Calibration Grid in the Real World
b.
Origin of the Same Calibration Grid in an Image
1
2
Figure 6-3. A Calibration Grid and an Image of the Grid
Note If you specify a list of points instead of a grid for the calibration process,
the software defines a default coordinate system, as follows:
1. The origin is placed at the point in the list with the lowest x-coordinate value and
then the lowest y-coordinate value.
2. The angle is set to 0°.
3. The axis direction is set to indirect.
If you define a coordinate system yourself, carefully consider the needs of
your application. Remember the following:
•
within the calibration grid so that you can convert the location to
real-world units.
•
Specify the angle as the angle between the x-axis of the new coordinate
system (x') and the top row of dots (x), as shown in Figure 6-4. If your
imaging system exhibits nonlinear distortion, you cannot visualize the
angle as you can in Figure 6-4 because the dots do not appear in
straight lines.
IMAQ Vision for LabWindows/CVI User Manual
6-4
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 6
Calibration
x
1
x'
x
2
y'
y
y
1
Default Origin in a Calibration Grid Image
2
User-Defined Origin
Learning Calibration Information
After you define a calibration grid and reference axis, acquire an image of
the grid using the current imaging setup. For information about acquiring
images, see the Acquire or Read an Image section of Chapter 2, Getting
Measurement-Ready Images. The grid does not need to occupy the entire
image. You can choose a region within the image that contains the grid.
After you acquire an image of the grid, learn the calibration information by
inputting the image of the grid into imaqLearnCalibrationGrid().
Note If you want to specify a list of points instead of a grid, use
imaqLearnCalibrationPoints() to learn the calibration information. Use the
CalibrationPoints structure to specify the pixel to real-world mapping.
© National Instruments Corporation
6-5
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 6
Calibration
Specifying Scaling Factors
Scaling factors are the real-world distances between the dots in the
calibration grid in the x and y directions and the units in which the distances
are measured. Use the GridDescriptor structure to specify the scaling
factors.
Choosing a Region of Interest
Define a learning region of interest (ROI) during the learning process to
define a region of the calibration grid you want to learn. The software
ignores dot centers outside this region when it estimates the transformation.
Creating a user-defined ROI is an effective way to increase correction
speeds depending on the other calibration options selected. Set the
user-defined ROI using the ROI parameter of either
imaqLearnCalibrationGrid() or
Choosing a Learning Algorithm
Select a method in which to learn the calibration information: perspective
projection or nonlinear. Figure 6-5 illustrates the types of errors your image
can exhibit. Figure 6-5a shows an image of a calibration grid with no
errors. Figure 6-5b shows an image of a calibration grid with perspective
projection. Figure 6-5c shows an image of a calibration grid with nonlinear
distortion.
a.
b.
c.
Figure 6-5. Types of Image Distortion
Choose the perspective projection algorithm when your system exhibits
perspective errors only. A perspective projection calibration has an accurate
transformation even in areas not covered by the calibration grid, as shown
in Figure 6-6. Set the mode element of the options parameter to
IMAQ Vision for LabWindows/CVI User Manual
6-6
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 6
Calibration
IMAQ_PERSPECTIVE to choose the perspective calibration algorithm.
Learning and applying perspective projection is less computationally
intensive than the nonlinear method. However, perspective projection
cannot handle nonlinear distortions.
If your imaging setup exhibits nonlinear distortion, use the nonlinear
method. The nonlinear method guarantees accurate results only in the area
that the calibration grid covers, as shown in Figure 6-6. If your system
exhibits both perspective and nonlinear distortion, use the nonlinear
method to correct for both. Set the mode element of the options parameter
to IMAQ_NONLINEAR to choose the nonlinear calibration algorithm.
2
1
1
Calibration ROI Using the
Perspective Algorithm
2
Calibration ROI Using the Nonlinear
Algorithm
Figure 6-6. Calibration ROIs
Using the Learning Score
The learning process returns a score that reflects how well the software
learned the input image. A high learning score indicates that you chose the
the appropriate learning algorithm, that the grid image complies with the
guideline, and that your vision system setup is adequate.
If the learning process returns a low score, try the following:
1. Make sure your grid complies with the guidelines listed in the
Defining a Calibration Template section.
2. Check the lighting conditions. If you have too much or too little
lighting, the software may estimate the center of the dots incorrectly.
© National Instruments Corporation
6-7
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 6
Calibration
Also, adjust the range parameter to distinguish the dots from the
background.
3. Select another learning algorithm. When nonlinear lens distortion is
present, using perspective projection sometimes results in a low
learning score.
Note A high score does not reflect the accuracy of your system.
Learning the Error Map
An error map helps you gauge the quality of your complete system.
The error map returns an estimated error range to expect when a pixel
coordinate is transformed into a real-world coordinate. The transformation
accuracy may be higher than the value the error range indicates. Set the
learnMap element of the options parameter to TRUE to learn the error
map.
Learning the Correction Table
If the speed of image correction is a critical factor for your application, use
a correction table. The correction table is a lookup table stored in memory
that contains the real-world location information of all the pixels in the
image. The extra memory requirements for this option are based on the size
of the image. Use this option when you want to correct several images at a
time in your vision application. Set the learnTable element of the
options parameter to TRUE to learn the correction table.
Setting the Scaling Method
Use the method element of the options parameter to choose the appearance
of the corrected image. Select either IMAQ_SCALE_TO_FIT or
IMAQ_SCALE_TO_PRESERVE_AREA. For more information about the
scaling methods, see Chapter 3, System Setup and Calibration, in the IMAQ
Vision Concepts Manual.
Calibration Invalidation
Any image processing operation that changes the image size or orientation
voids the calibration information in a calibrated image. Examples of
functions that void calibration information include imaqResample(),
IMAQ Vision for LabWindows/CVI User Manual
6-8
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 6
Calibration
Simple Calibration
When the axis of your camera is perpendicular to the image plane and lens
distortion is negligible, use simple calibration. In simple calibration, a pixel
coordinate is transformed to a real-world coordinate through scaling in the
horizontal and vertical directions.
Use simple calibration to map pixel coordinates to real-world coordinates
directly without a calibration grid. The software rotates and scales a pixel
factors. You can assign the calibration to an arbitrary image using
imaqSetSimpleCalibration().
To perform a simple calibration, set a coordinate reference (angle, center,
and axis direction) and scaling factors on the defined axis, as shown in
Figure 6-7. Express the angle between the x-axis and the horizontal axis
of the image in degrees. Express the center as the position, in pixels, where
you want the coordinate reference origin. Set the axis direction to direct or
indirect. Simple calibration also offers a correction table option and a
scaling mode option.
Use the system parameter to define the coordinate system. Use the grid
parameter to specify the scaling factors. Use the method parameter to set
the scaling method. Set the learnTable parameter to TRUE to learn the
correction table.
Y
X
dy
1
dx
1
Origin
Figure 6-7. Defining a Simple Calibration
© National Instruments Corporation
6-9
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Chapter 6
Calibration
Save Calibration Information
After you learn the calibration information, you can save it so that you do
not have to relearn the information for subsequent processing. Use
imaqWriteVisionFile() to save the image of the grid and its associated
calibration information to a file. To read the file containing the calibration
information use imaqReadVisionFile(). For more information about
attaching the calibration information you read from another image, see the
Attach Calibration Information section.
Attach Calibration Information
Now that you have calibrated your setup correctly, you can apply
the calibration settings to images that you acquire. Use
imaqCopyCalibrationInfo() to attach the calibration information of
the current setup to each image you acquire. This function takes in a source
image containing the calibration information and a destination image that
you want to calibrate. The destination image is your inspection image with
the calibration information attached to it.
Using the calibration information attached to the image, you can accurately
convert pixel coordinates to real-world coordinates to make any of the
analytic geometry measurements with
imaqTransformPixelToRealWorld(). If your application requires that
you make shape measurements, correct the image by removing distortion
with imaqCorrectImage().
Note Because calibration information is part of the image, it is propagated throughout
the processing and analysis of the image. Functions that modify the image size (such as an
image rotation function) void the calibration information. Use imaqWriteVisionFile()
to save the image and all of the attached calibration information to a file.
IMAQ Vision for LabWindows/CVI User Manual
6-10
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
A
Technical Support Resources
Web Support
National Instruments Web support is your first stop for help in solving
installation, configuration, and application problems and questions. Online
problem-solving and diagnostic resources include frequently asked
questions, knowledge bases, product-specific troubleshooting wizards,
manuals, drivers, software updates, and more. Web support is available
through the Technical Support section of ni.com
NI Developer Zone
The NI Developer Zone at ni.com/zone is the essential resource for
building measurement and automation systems. At the NI Developer Zone,
you can easily access the latest example programs, system configurators,
tutorials, technical news, as well as a community of developers ready to
share their own techniques.
Customer Education
National Instruments provides a number of alternatives to satisfy your
training needs, from self-paced tutorials, videos, and interactive CDs to
instructor-led hands-on courses at locations around the world. Visit the
Customer Education section of ni.com for online course schedules,
syllabi, training centers, and class registration.
System Integration
If you have time constraints, limited in-house technical resources, or other
dilemmas, you may prefer to employ consulting or system integration
services. You can rely on the expertise available through our worldwide
network of Alliance Program members. To find out more about our
Alliance system integration solutions, visit the System Integration section
of ni.com
© National Instruments Corporation
A-1
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Appendix A
Technical Support Resources
Worldwide Support
National Instruments has offices located around the world to help address
your support needs. You can access our branch office Web sites from the
Worldwide Offices section of ni.com. Branch office Web sites provide
up-to-date contact information, support phone numbers, e-mail addresses,
and current events.
If you have searched the technical support resources on our Web site and
still cannot find the answers you need, contact your local office or National
Instruments corporate. Phone numbers for our worldwide offices are listed
at the front of this manual.
IMAQ Vision for LabWindows/CVI User Manual
A-2
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
Numbers/Symbols
1D
2D
3D
One-dimensional.
Two-dimensional.
Three-dimensional.
A
AIPD
National Instruments proprietary image file format used for saving
complex images and calibration information pertaining to step and
spatial units (extension APD).
alignment
The process by which a machine vision application determines the location,
orientation, and scale of a part being inspected.
alpha channel
Channel used to code extra information, such as gamma correction, about
a color image. The alpha channel is stored as the first byte in the four-byte
representation of an RGB pixel.
annulus
area
A region of interest that resembles a ring or partial ring.
(1) A rectangular portion of an acquisition window or frame that is
controlled and defined by software. (2) The size of an object in pixels or
user-defined units.
area threshold
Detects objects based on their size, which can fall within a user-specified
range.
arithmetic operators
array
The image operations multiply, divide, add, subtract, and remainder.
Ordered, indexed set of data elements of the same type.
auto-median function
A function that uses dual combinations of opening and closing operations
to smooth the boundaries of objects.
© National Instruments Corporation
G-1
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
B
b
Bit. One binary digit, either 0 or 1.
B
Byte. Eight related bits of data, an eight-bit binary number. Also denotes
the amount of memory required to store one byte of data.
barycenter
The grayscale value representing the centroid of the range of an image's
grayscale values in the image histogram.
binary image
An image in which the objects usually have a pixel intensity of 1 (or 255)
and the background has a pixel intensity of 0.
binary morphology
binary threshold
Functions that perform morphological operations on a binary image.
Separation of an image into objects of interest (assigned a non-zero pixel
value) and background (assigned pixel values of 0) based on the intensities
of the image pixels.
bit depth
The number of bits (n) used to encode the value of a pixel. For a given n,
can take 256 different values ranging from 0 to 255. If n equals 16 bits, a
pixel can take 65,536 different values ranging from 0 to 65,535 or –32,768
to 32,767.
black reference level
blob
The level that represents the darkest an image can get. See also white
reference level.
Binary large object. A connected region or grouping of pixels in an image
in which all pixels have the same intensity level. Blobs are also referred to
as objects or particles.
blob analysis
blurring
A series of processing operations and analysis functions that produce some
information about the blobs in an image.
Reduces the amount of detail in an image. Blurring commonly occurs
because the camera is out of focus. You can blur an image intentionally
by applying a lowpass frequency filter.
BMP
Bitmap. Image file format commonly used for 8-bit and color images
(extension BMP).
IMAQ Vision for LabWindows/CVI User Manual
G-2
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
brightness
buffer
(1) A constant added to the red, green, and blue components of a color pixel
during the color decoding process. (2) The perception by which white
objects are distinguished from gray and light objects from dark objects.
Temporary storage for acquired data.
C
caliper
(1) A function in IMAQ Vision Builder that calculates distances, angles,
circular fits, and the center of mass based on positions given by edge
detection, particle analysis, centroid, and search functions.
(2) A measurement function that finds edge pairs along a specified path in
the image. This function performs an edge extraction and then finds edge
pairs based on specified criteria such as the distance between the leading
and trailing edges, edge contrasts, and so forth.
center of mass
centroid
The point on an object where all the mass of the object could be
concentrated without changing the first moment of the object about any
axis
(1) The average of the x-coordinates and y-coordinates of a binary image
or a blob in the image. The centroid of a blob may lie outside the blob.
(2) The weighted average of the x-coordinates and y-coordinates in a
grayscale image, where the weights are determined by the pixel values in
the image.
character recognition
chroma
The ability of a machine to read human-readable text.
The color information in a video signal.
chromaticity
The combination of hue and saturation. The relationship between
chromaticity and brightness characterizes a color.
circle function
closing
Detects circular objects in a binary image.
A dilation followed by an erosion. A closing fills small holes in objects
and smooths the boundaries of objects.
clustering
A technique where the image is sorted into a discrete number of classes.
Each class contains pixels that fall within a distinct range of grayscale
values. The barycenter is determined for each class. This process is
repeated until a value is obtained that represents the center of mass for each
phase or class.
© National Instruments Corporation
G-3
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
CLUT
Color lookup table. Table for converting the value of a pixel in an image
into a red, green, and blue (RGB) intensity.
color images
color location
Images containing color information, usually encoded in the RGB form.
The technique that locates a color template in a color image based on only
the color information.
color matching
color pattern matching
color space
The technique that compares the color information in an image or region of
an image to the color information in another image or region of an image.
The technique that locates a color template in a color image based on both
the color information and grayscale pattern in the template.
The mathematical representation for a color. For example, color can be
described in terms of red, green, and blue; hue, saturation, and luminance;
or hue, saturation, and intensity.
complex image
Stores information obtained from the FFT of an image. The complex
numbers that compose the FFT plane are encoded in 64-bit floating-point
values: 32bits for the real part and 32bits for the imaginary part.
connectivity
Defines which of the surrounding pixels of a given pixel constitute its
neighborhood.
connectivity-4
Only pixels adjacent in the horizontal and vertical directions are considered
neighbors.
connectivity-8
contrast
All adjacent pixels are considered neighbors.
(1) A constant multiplication factor applied to the luma and chroma
components of a color pixel in the color decoding process.
(2) The difference between light and dark intensity values in an image.
convex function
convex hull
Computes the convex regions of objects in a binary image.
The smallest convex polygon that can encapsulate a particle.
See linear filter.
convolution
convolution kernel
2D matrices (or templates) used to represent the filter in the filtering
process. The contents of these kernels are a discrete two-dimensional
representation of the impulse response of the filter that they represent.
IMAQ Vision for LabWindows/CVI User Manual
G-4
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
cross correlation
A technique that compares the similarity of two images or parts of an
image. You can use cross correlation to find the optimal position where
similarity exists.
D
Danielsson function
Similar to the distance functions, but with more accurate results.
dB
Decibel. The unit for expressing a logarithmic measure of the ratio of
two signal levels: dB = 20log10 V1/V2, for signals in volts.
default setting
definition
A default parameter value recorded in the driver. In many cases, the default
input of a control is a certain value (often 0).
The number of values a pixel can take on, which is the number of colors or
shades that you can see in the image.
dendrite
Branches of the skeleton of an object.
densitometry
Intensity information about an image or regions of an image. Typical
measurements include minimum, maximum, and mean intensity values
as well as the standard deviation of the intensity values.
density function
For each gray level in a linear histogram, the function gives the number of
pixels in the image that have the same gray level.
differentiation filter
digital image
Extracts the contours (edge detection) in gray level.
An image f (x, y) that has been converted into a discrete number of pixels.
Both spatial coordinates and brightness are specified.
dilation
Increases the size of an object along its boundary and removes tiny holes
in the object.
distance calibration
distance function
driver
Determination of the physical dimensions of a pixel by defining the
physical dimensions of a line in the image.
Assigns to each pixel in an object a gray-level value equal to its shortest
Euclidean distance from the border of the object.
Software that controls a specific hardware device, such as an IMAQ or
DAQ device.
© National Instruments Corporation
G-5
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
E
edge
Defined by a sharp change (transition) in the pixel intensities in an image
or along an array of pixels.
edge contrast
The difference between the average pixel intensity before and the average
pixel intensity after the edge.
edge detection
edge hysteresis
edge steepness
Any of several techniques to identify the edges of objects in an image.
The difference in threshold level between a rising and a falling edge.
The number of pixels that corresponds to the slope or transition area of an
edge.
energy center
entropy
The center of mass of a grayscale image. See center of mass.
A measure of the randomness in an image. An image with high entropy
contains more pixel value variation than an image with low entropy.
equalize function
erosion
See histogram equalization.
Reduces the size of an object along its boundary and eliminates isolated
points in the image.
Euclidean distance
The shortest distance between two points in a Cartesian system.
exponential and
gamma corrections
Expand the high gray-level information in an image while suppressing
low gray-level information.
exponential function
Decreases brightness and increases contrast in bright regions of an image,
and decreases contrast in dark regions of an image.
F
feature
A specific pattern of pixels in an image.
FFT
Fast Fourier Transform. A method used to compute the Fourier transform
of an image to get frequency information.
fiducial
A reference pattern on a part that helps a machine vision application find
the part's location and orientation in an image.
IMAQ Vision for LabWindows/CVI User Manual
G-6
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
form
Window or area on the screen on which you place controls and indicators
to create the user interface for your program.
Fourier spectrum
Fourier transform
frequency filters
The magnitude information of the Fourier transform of an image.
Transforms an image from the spatial domain to the frequency domain.
Counterparts of spatial filters in the frequency domain. For images,
frequency information is in the form of spatial frequency.
function
A set of software instructions executed by a single line of code that may
have input and/or output parameters and returns a value when executed.
G
gamma
The nonlinear change in the difference between the video signal's
brightness level and the voltage level needed to produce that brightness.
gauging
Gaussian filter
A filter similar to the smoothing filter, but using a Gaussian kernel in the
filter operation. The blurring in a Gaussian filter is more gentle than a
smoothing filter.
gradient convolution
filter
See gradient filter.
gradient filter
Extracts the contours (edge detection) in gray-level values. Gradient filters
include the Prewitt and Sobel filters.
gray level
The brightness of a pixel in an image.
gray-level dilation
Increases the brightness of pixels in an image that are surrounded by other
pixels with a higher intensity.
gray-level erosion
grayscale image
Reduces the brightness of pixels in an image that are surrounded by other
pixels with a lower intensity.
An image with monochrome information.
grayscale
Functions that perform morphological operations on a gray-level image.
morphology
© National Instruments Corporation
G-7
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
H
highpass attenuation
Applies a linear attenuation to the frequencies in an image, with no
attenuation at the highest frequency and full attenuation at the lowest
frequency.
highpass FFT filter
highpass filter
Removes or attenuates low frequencies present in the FFT domain of an
image.
Emphasizes the intensity variations in an image, detects edges (or object
boundaries), and enhances fine details in an image.
highpass frequency
filter
Attenuates or removes (truncates) low frequencies present in the frequency
domain of the image. A highpass frequency filter suppresses information
related to slow variations of light intensities in the spatial image.
highpass truncation
histogram
Removes all frequency information below a certain frequency.
Indicates the quantitative distribution of the pixels of an image per
gray-level value.
histogram
equalization
Transforms the gray-level values of the pixels of an image to occupy the
entire range (0 to 255 in an 8-bit image) of the histogram, increasing the
contrast of the image.
histogram inversion
hit-miss function
Finds the inverse of an image. The histogram of a reversed image is equal
to the original histogram flipped horizontally around the center of the
histogram.
Locates objects in the image similar to the pattern defined in the structuring
element.
hole filling function
Fills all holes in objects that are present in a binary image.
Color encoding scheme in Hue, Saturation, and Intensity.
HSI
HSL
Color encoding scheme using Hue, Saturation, and Luma information
where each image in the pixel is encoded using 32 bits: 8 bits for hue,
HSV
hue
Color encoding scheme in Hue, Saturation, and Value.
Represents the dominant color of a pixel. The hue function is a continuous
function that covers all the possible colors generated using the R, G, and
B primaries. See also RGB.
IMAQ Vision for LabWindows/CVI User Manual
G-8
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
hue offset angle
The value added to all hue values so that the discontinuity occurs outside
the values of interest during analysis.
I
image
A two-dimensional light intensity function f (x, y) where x and y denote
spatial coordinates and the value f at any point (x, y) is proportional to the
brightness at that point.
image border
A user-defined region of pixels surrounding an image. Functions that
process pixels based on the value of the pixel neighbors require image
borders.
Image Browser
An image that contains thumbnails of images to analyze or process in a
vision application.
image buffer
Memory location used to store images.
image definition
image enhancement
See pixel depth.
The process of improving the quality of an image that you acquire from
a sensor in terms of signal-to-noise ratio, image contrast, edge definition,
and so on.
image file
A file containing pixel data and additional information about the image.
image format
Defines how an image is stored in a file. Usually composed of a header
followed by the pixel data.
image mask
A binary image that isolates parts of a source image for further processing.
A pixel in the source image is processed if its corresponding mask pixel has
a non-zero value. A source pixel whose corresponding mask pixel has a
value of 0 is left unchanged.
image palette
The gradation of colors used to display an image on screen, usually defined
by a color lookup table.
image processing
Encompasses various processes and analysis functions that you can apply
to an image.
image source
Original input image.
image understanding
A technique that interprets the content of the image at a symbolic level
rather than a pixel level.
© National Instruments Corporation
G-9
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
image visualization
imaging
The presentation (display) of an image (image data) to the user.
Any process of acquiring and displaying images and analyzing image data.
Image Acquisition.
IMAQ
inner gradient
inspection
Finds the inner boundary of objects.
The process by which parts are tested for simple defects such as missing
parts or cracks on part surfaces.
inspection function
Analyzes groups of pixels within an image and returns information about
the size, shape, position, and pixel connectivity. Typical applications
include quality of parts, analyzing defects, locating objects, and sorting
objects.
instrument driver
A set of high-level software functions, such as NI-IMAQ, that control
specific plug-in computer boards. Instrument drivers are available in
several forms, ranging from a function callable from a programming
language to a virtual instrument (VI) in LabVIEW.
intensity
(1) The sum of the Red, Green, and Blue primary colors divided by three,
(Red + Green + Blue)/3 in a color image. (2) The gray-level value of a pixel
in a grayscale image.
intensity calibration
Assigning user-defined quantities, such as optical densities or
concentrations, to the gray-level values in an image.
intensity profile
intensity range
The gray-level distribution of the pixels in an ROI of an image.
Defines the range of gray-level values in an object of an image.
intensity threshold
Characterizes an object based on the range of gray-level values in the
object. If the intensity range of the object falls within the user-specified
range, it is considered an object. Otherwise it is considered part of the
background.
interpolation
IRE
The technique used to find values in between known values when
resampling an image or array of pixels.
A relative unit of measure (named for the Institute of Radio Engineers).
0 IRE corresponds to the blanking level of a video signal, 100 IRE to the
blanking level or 0 IRE, while for RS-170/NTSC video, the black level is
at 7.5 IRE.
IMAQ Vision for LabWindows/CVI User Manual
G-10
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
J
JPEG
Joint Photographic Experts Group. Image file format for storing 8-bit and
color images with lossy compression (extension JPG).
K
kernel
Structure that represents a pixel and its relationship to its neighbors. The
relationship is specified by weighted coefficients of each neighbor.
L
labeling
The process by which each object in a binary image is assigned a unique
value. This process is useful for identifying the number of objects in the
image and giving each object a unique identity.
LabVIEW
Laboratory Virtual Instrument Engineering Workbench. Program
development environment application based on the programming language
G used commonly for test and measurement applications.
Laplacian filter
line gauge
Extracts the contours of objects in the image by highlighting the variation
of light intensity surrounding a pixel.
Measures the distance between selected edges with high-precision subpixel
accuracy along a line in an image. For example, this function can be used
to measure distances between points and edges. This function also can step
and repeat its measurements across the image.
line profile
linear filter
Represents the gray-level distribution along a line of pixels in an image.
A special algorithm that calculates the value of a pixel based on its own
pixel value as well as the pixel values of its neighbors. The sum of this
calculation is divided by the sum of the elements in the matrix to obtain a
new pixel value.
logarithmic and inverse Expand low gray-level information in an image while compressing
gamma corrections
information from the high gray-level ranges.
logarithmic function
Increases the brightness and contrast in dark regions of an image and
decreases the contrast in bright regions of the image.
© National Instruments Corporation
G-11
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
logic operators
The image operations AND, NAND, OR, XOR, NOR, XNOR, difference,
mask, mean, max, and min.
lossless compression
lossy compression
lowpass attenuation
Compression in which the decompressed image is identical to the original
image.
Compression in which the decompressed image is visually similar but not
identical to the original image.
Applies a linear attenuation to the frequencies in an image, with no
attenuation at the lowest frequency and full attenuation at the highest
frequency.
lowpass FFT filter
lowpass filter
Removes or attenuates high frequencies present in the FFT domain of an
image.
Attenuates intensity variations in an image. You can use these filters to
smooth an image by eliminating fine details and blurring edges.
lowpass frequency filter Attenuates high frequencies present in the frequency domain of the image.
A lowpass frequency filter suppresses information related to fast variations
of light intensities in the spatial image.
lowpass truncation
LSB
Removes all frequency information above a certain frequency.
Least significant bit.
L-skeleton function
luma
The brightness information in the video picture. The luma signal amplitude
varies in proportion to the brightness of the video signal and corresponds
exactly to the monochrome picture.
luminance
LUT
See luma.
Lookup table. Table containing values used to transform the gray-level
values of an image. For each gray-level value in the image, the
corresponding new value is obtained from the lookup table.
M
machine vision
An automated application that performs a set of visual inspection tasks.
Removes frequencies contained in a mask (range) specified by the user.
mask FFT filter
IMAQ Vision for LabWindows/CVI User Manual
G-12
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
match score
A number ranging from 0 to 1000 that indicates how closely an acquired
image matches the template image. A match score of 1000 indicates a
median filter
memory buffer
A lowpass filter that assigns to each pixel the median value of its neighbors.
This filter effectively removes isolated pixels without blurring the contours
of objects.
See buffer.
morphological
transformations
Extract and alter the structure of objects in an image. You can use these
transformations for expanding (dilating) or reducing (eroding) objects,
filling holes, closing inclusions, or smoothing borders. They are used
primarily to delineate objects and prepare them for quantitative inspection
analysis.
MSB
Most significant bit.
M-skeleton function
Uses an M-shaped structuring element in the skeleton function.
N
neighbor
A pixel whose value affects the value of a nearby pixel when an image is
processed. The neighbors of a pixel are usually defined by a kernel or a
structuring element.
neighborhood
operations
Operations on a point in an image that take into consideration the values of
the pixels neighboring that point.
NI-IMAQ
Driver software for National Instruments IMAQ hardware.
nonlinear filter
Replaces each pixel value with a nonlinear function of its surrounding
pixels.
nonlinear gradient filter A highpass edge-extraction filter that favors vertical edges.
nonlinear Prewitt filter
A highpass, edge-extraction filter based on two-dimensional gradient
information.
nonlinear Sobel filter
A highpass, edge-extraction filter based on two-dimensional gradient
information. The filter has a smoothing effect that reduces noise
enhancements caused by gradient operators.
© National Instruments Corporation
G-13
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
Nth order filter
Filters an image using a nonlinear filter. This filter orders (or classifies)
the pixel values surrounding the pixel being processed. The pixel being
processed is set to the Nth pixel value, where N is the order of the filter.
number of planes
(in an image)
The number of arrays of pixels that compose the image. A gray-level or
pseudo-color image is composed of one plane, while an RGB image is
composed of three planes (one for the red component, one for the blue,
and one for the green).
O
object
A connected region or grouping of pixels in an image in which all pixels
have the same intensity level. Objects are also referred to as blobs or
particles.
offset
The coordinate position in an image where you want to place the origin of
another image. Setting an offset is useful when performing mask
operations.
opening
An erosion followed by a dilation. An opening removes small objects and
smooths boundaries of objects in the image.
operators
Allow masking, combination, and comparison of images. You can use
arithmetic and logic operators in IMAQ Vision.
optical character
verification
A machine vision application that inspects the quality of printed characters.
optical representation
Contains the low-frequency information at the center and the high-
frequency information at the corners of an FFT-transformed image.
outer gradient
overlay
Finds the outer boundary of objects.
ROIs, text, and bitmaps that you can place on top of a displayed image to
annotate it without modifying it.
P
palette
The gradation of colors used to display an image on screen, usually defined
IMAQ Vision for LabWindows/CVI User Manual
G-14
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
particle
A connected region or grouping of pixels in an image in which all pixels
have the same intensity level. Particles are also referred to as blobs or
objects.
pattern matching
The technique used to quickly locate a grayscale template within a
grayscale image
picture aspect ratio
The ratio of the active pixel region to the active line region. For standard
video signals like RS-170 or CCIR, the full-size picture aspect ratio
normally is 4/3 (1.33).
picture element
pixel
An element of a digital image. Also called pixel.
Picture element. The smallest division that makes up the video scan line.
For display on a computer monitor, a pixel's optimum dimension is square
(aspect ratio of 1:1, or the width equal to the height).
pixel aspect ratio
The ratio between the physical horizontal size and the vertical size of the
region covered by the pixel. An acquired pixel should optimally be square,
thus the optimal value is 1.0, but typically it falls between 0.95 and 1.05,
depending on camera quality.
pixel calibration
pixel depth
PNG
Directly calibrating the physical dimensions of a pixel in an image.
The number of bits used to represent the gray level of a pixel.
Portable Network Graphic. Image file format for storing 8-bit, 16-bit,
and color images with lossless compression (extension PNG).
power 1/Y function
power Y function
Prewitt filter
Similar to a logarithmic function but with a weaker effect.
See exponential function.
Extracts the contours (edge detection) in gray-level values using a
3 x 3 filter kernel. See gradient filter.
probability function
proper-closing
Defines the probability that a pixel in an image has a certain gray-level
value.
A finite combination of successive closing and opening operations that you
can use to fill small holes and smooth the boundaries of objects.
proper-opening
A finite combination of successive opening and closing operations that you
can use to remove small particles and smooth the boundaries of objects.
© National Instruments Corporation
G-15
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
pyramidal matching
A technique used to increase the speed of a pattern matching algorithm by
matching subsampled versions of the image and the reference pattern.
Q
quantitative analysis
Obtaining various measurements of objects in an image.
R
real time
A property of an event or system in which data is processed as it is acquired
instead of being accumulated and processed at a later time.
relative accuracy
A measure in LSB of the accuracy of an ADC; it includes all nonlinearity
and quantization errors but does not include offset and gain errors of the
circuitry feeding the ADC.
remove border objects
function
Removes blobs in a binary image that touch the image border.
resolution
The number of rows and columns of pixels. An image composed of m rows
and n columns has a resolution of m × n.
reverse function
RGB
Inverts the pixel values in an image.
Color encoding scheme using red, green, and blue (RGB) color information
where each pixel in the color image is encoded using 32 bits: 8 bits for red,
8 bits for green, 8 bits for blue, and 8 bits for the alpha value (unused).
Roberts filter
ROI
Extracts the contours (edge detection) in gray level, favoring diagonal
edges.
Region of interest. (1) An area of the image that is graphically selected
from a window displaying the image. This area can be used to focus further
processing. (2) A hardware-programmable rectangular portion of the
acquisition window.
ROI tools
Collection of tools that enable you to select a region of interest from an
image. These tools let you select points, lines, annuli, polygons, rectangles,
rotated rectangles, ovals, and freehand open and closed contours.
rotational shift
The amount by which one image is rotated with respect to a reference
image. This rotation is computed with respect to the center of the image.
IMAQ Vision for LabWindows/CVI User Manual
G-16
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
rotation-invariant
matching
A pattern matching technique in which the reference pattern can be located
at any orientation in the test image as well as rotated at any degree.
S
saturation
The amount of white added to a pure color. Saturation relates to the richness
of a color. A saturation of zero corresponds to a pure color with no white
added. Pink is a red with low saturation.
scale-invariant
matching
A pattern matching technique in which the reference pattern can be any size
in the test image. See shape matching.
segmentation function
Fully partitions a labeled binary image into non-overlapping segments,
with each segment containing a unique object.
separation function
shape matching
Separates objects that touch each other by narrow isthmuses.
Finds objects in an image whose shape matches the shape of the object
specified by a shape template. The matching process is invariant to rotation
and can be set to be invariant to the scale of the objects.
shift-invariant
matching
A pattern matching technique in which the reference pattern can be located
anywhere in the test image but cannot be rotated or scaled.
Sigma filter
A highpass filter that outlines edges.
skeleton function
Applies a succession of thinning operations to an object until its width
becomes one pixel.
skiz function
smoothing filter
Sobel filter
Obtains lines in an image that separate each object from the others and are
equidistant from the objects that they separate.
Blurs an image by attenuating variations of light intensity in the
neighborhood of a pixel.
Extracts the contours (edge detection) in gray-level values using a
3 × 3 filter kernel. See gradient filter.
spatial calibration
spatial filters
Assigning physical dimensions to the area of a pixel in an image.
Alter the intensity of a pixel with respect to variations in intensities of its
enhancement, noise reduction, smoothing, and so forth.
© National Instruments Corporation
G-17
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
spatial resolution
The number of pixels in an image, in terms of the number of rows and
columns in the image.
square function
See exponential function.
square root function
standard representation
See logarithmic function.
Contains the low-frequency information at the corners and high-frequency
information at the center of an FFT-transformed image.
structuring element
sub-pixel analysis
A binary mask used in most morphological operations. A structuring
element is used to determine which neighboring pixels contribute in the
operation.
Finds the location of the edge coordinates in terms of fractions of a pixel.
T
template
Color, shape, or pattern that you are trying to match in an image using the
color matching, shape matching, or pattern matching functions. A template
can be a region selected from an image or it can be an entire image.
thickening
thinning
Alters the shape of objects by adding parts to the object that match the
pattern specified in the structuring element.
Alters the shape of objects by eliminating parts of the object that match the
pattern specified in the structuring element.
threshold
Separates objects from the background by assigning all pixels with
intensities within a specified range to the object and the rest of the pixels to
the background. In the resulting binary image, objects are represented with
a pixel intensity of 255 and the background is set to 0.
threshold interval
TIFF
Two parameters, the lower threshold gray-level value and the upper
threshold gray-level value.
Tagged Image File Format. Image format commonly used for encoding
8-bit, 16-bit, and color images (extension TIF).
Tools palette
Collection of tools that enable you to select regions of interest, zoom in and
out, and change the image palette.
IMAQ Vision for LabWindows/CVI User Manual
G-18
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Glossary
V
value
The grayscale intensity of a color pixel computed as the average of the
maximum and minimum red, green, and blue values of that pixel.
W
watershed
A technique used to segment an image into multiple regions.
web inspection
The process of detecting defects in a continuous sheet of materials at
production speeds. Example materials include plastic film, cloth, paper
and pulp products, metal, and glass.
white reference level
The level that defines what is white for a particular video system.
See also black reference level.
© National Instruments Corporation
G-19
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Index
A
C
acquiring measurement-ready images.
See measurement-ready images, acquiring.
analytic geometry measurements, 5-27
analyzing images, 2-8 to 2-9
Annulus tool (table), 3-2
calibration, 6-1 to 6-10
attaching calibration information to images,
2-8, 6-10
defining reference coordinate system,
6-2 to 6-5
defining template, 6-2
application development, 1-5 to 1-6
general steps (figure), 1-5
inspection steps (figure), 1-6
application development environments
supported, 1-2
learning calibration information, 6-5 to 6-8
choosing learning algorithm, 6-6 to 6-7
choosing ROI, 6-6
correction table, 6-8
array, converting to image, 2-7
attaching calibration information to images,
2-8, 6-10
error map, 6-8
invalidation of calibration, 6-8
setting scaling method, 6-8
specifying scaling factors, 6-6
using learning score, 6-7 to 6-8
overview, 2-2
attenuation
highpass, 2-12
lowpass, 2-12
for perspective and nonlinear distortion,
6-1 to 6-8
saving calibration information, 6-10
simple calibration, 6-9
B
binary images. See blob analysis.
blob analysis, 4-1 to 4-7
CalibrationPoints structure, 6-5
circles, finding points along edge, 5-9 to 5-11
color comparison, 3-8 to 3-9
color information, learning. See learning color
information.
converting pixel coordinates to real-world
coordinates, 4-7
correcting image distortion, 4-2
creating binary image, 4-2
improving binary image, 4-3 to 4-4
improving blob shapes, 4-4
removing unwanted blobs, 4-3 to 4-4
separating touching blobs, 4-4
particle measurements, 4-4 to 4-7
steps (figure), 4-1
color location algorithms for finding
measurement points, 5-25
color measurements. See grayscale and color
measurements.
color pattern matching, 5-18 to 5-25. See also
pattern matching.
defining search area, 5-21 to 5-22
Broken Line tool (table), 3-2
© National Instruments Corporation
I-1
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Index
defining template images, 5-19 to 5-20
setting matching parameters and
tolerances, 5-22 to 5-24
coordinates, converting pixel to real-world,
4-7, 5-26
Coordinatesystem structure, 6-2
correction table, for calibration, 6-8
creating applications. See application
development.
color score weight, 5-24
color sensitivity, 5-23
minimum contrast, 5-24
creating images. See images.
customer education, A-1
rotation angle ranges, 5-24
search strategy, 5-23 to 5-24
testing search algorithm on test images,
5-24 to 5-25
training pattern matching algorithm,
5-20 to 5-21
D
destination images, 2-4 to 2-5
displaying
color statistics, 3-7 to 3-13
comparing colors, 3-8 to 3-9
learning color information, 3-9 to 3-13
choosing color representation
sensitivity, 3-12
images, 2-7
results of inspection process, 5-28 to 5-29
distance measurements, 5-26
distortion, correcting. See calibration.
documentation
choosing right color information,
3-9 to 3-10
ignoring learned colors, 3-13
specifying information to learn,
3-10 to 3-12
conventions used in manual, iv
documentation resources and examples,
1-1 to 1-2
using entire image, 3-10
using multiple regions in image,
3-11 to 3-12
E
edge detection
using region in image, 3-10 to 3-11
primary components of color image
(figure), 3-8
building coordinate transform, 5-3 to 5-5
finding measurement points, 5-9 to 5-12
along multiple search contours, 5-12
along one search contour,
comparing colors, 3-8 to 3-9
contour, finding points along edge,
5-11 to 5-12
conventions used in manual, iv
converting array to image, 2-7
convolution filters, 2-10
coordinate reference for calibration, defining,
6-2 to 6-5
coordinate transform, building
choosing method (figure), 5-7
edge detection, 5-3 to 5-5
pattern matching, 5-5 to 5-6
5-11 to 5-12
lines or circles, 5-9 to 5-11
error map, for calibration, 6-8
F
Fast Fourier Transform (FFT), 2-11 to 2-13
filters
convolution, 2-10
highpass, 2-10
highpass frequency, 2-12
improving images, 2-10 to 2-11
IMAQ Vision for LabWindows/CVI User Manual
I-2
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Index
lowpass, 2-10
lowpass frequency, 2-12
Nth order, 2-11
I
ignoring learned colors, 3-13
images. See also blob analysis.
acquiring or reading, 2-5 to 2-7
analyzing, 2-8 to 2-9
finding measurement points. See measurement
points, finding.
Freehand Line tool (table), 3-2
Freehand tool (table), 3-3
frequency domain, 2-11
function tree, 1-2 to 1-4
IMAQ Machine Vision function types
(table), 1-4
attaching calibration information,
2-8, 6-10
converting array to image, 2-7
creating, 2-2 to 2-5
image pointers, 2-3
mask parameter, 2-5
multiple images, 2-3
overview, 2-2 to 2-3
IMAQ Vision function types (table),
1-3 to 1-4
passing NULL for image mask, 2-5
source and destination images,
2-4 to 2-5
G
geometrical measurements, 5-27
grayscale and color measurements, 3-1 to 3-13
color statistics, 3-7 to 3-13
comparing colors, 3-8 to 3-9
learning color information,
3-9 to 3-13
valid image types (table), 2-3
displaying, 2-7
improving, 2-9 to 2-13
complex operations, 2-13
FFT (Fast Fourier Transform),
2-11 to 2-13
primary components of color image
(figure), 3-8
defining regions of interest, 3-1 to 3-7
interactively, 3-1 to 3-6
programmatically, 3-6
using masks, 3-6 to 3-7
grayscale statistics, 3-7
filters, 2-10 to 2-11
grayscale morphology, 2-11
lookup tables, 2-9 to 2-10
imaging system
calibrating, 2-2
setting up, 2-1 to 2-2
IMAQ Machine Vision function tree, 1-4
IMAQ Vision for Measurement Studio
application development
environments, 1-2
grayscale morphology, 2-11
grayscale statistics, 3-7
GridDescriptor structure, 6-6
creating applications, 1-5 to 1-6
general steps (figure), 1-5
inspection steps (figure), 1-6
function trees, 1-2 to 1-4
IMAQ Machine Vision, 1-4
IMAQ Vision function types (table),
1-3 to 1-4
H
highpass filters, 2-10
highpass frequency filters
attenuation, 2-12
truncation, 2-12
overview, 1-1
© National Instruments Corporation
I-3
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Index
imaqAdd() function, 2-4 to 2-5
imaqFillHoles() function, 4-4
imaqAddRectContour() function, 3-6
imaqArrayToComplextPlane() function, 2-13
imaqArrayToImage() function, 2-7, 6-8
imaqAttenuate() function, 2-12
IMAQ_AUTOM method, 4-4
imaqAutoThreshold() function, 4-2
imaqCalcCoeff() function, 4-5
particle measurements returned (table),
4-5 to 4-7
imaqCannyEdgeFilter() function, 2-10
imaqCentroid() function, 3-7
imaqFindCircularEdge() function, 5-11, 5-29
imaqFindConcentricEdges() function,
5-9, 5-29
imaqFindEdge() function, 5-9, 5-29
imaqFindLCDSegments() function, 5-28
imaqFindPattern() function, 5-29
imaqFindTransformPattern() function,
5-5, 5-29
imaqFindTransformRect() function, 5-3, 5-29
imaqFindTransformRects() function,
5-4, 5-29
imaqFitCircle() function, 5-27
imaqFitEllipse() function, 5-27
imaqFitLine() function, 5-27
imaqClampMax() function, 5-26, 5-29
imaqClampMin() function, 5-26, 5-29
imaqClearOverlay() function, 5-29
IMAQ_CLOSE method, 4-4
imaqCloseToolWindow() function, 3-5
imaqCloseWindow() function, 2-7
imaqColorThreshold() function, 4-2
imaqComplexPlaneToArray() function, 2-13
imaqConcentricRake() function, 5-12
imaqConstructROI() function, 3-3, 5-8
imaqConvolve() function, 2-10
imaqGetAngle() function, 5-27
imaqGetBisectingLine() function, 5-27
imaqGetDistance() function, 5-26
imaqGetFileInfo() function, 2-7
imaqGetIntersection() function, 5-27
imaqGetKernel() function, 2-10
imaqGetMeterArc() function, 5-27
imaqGetMidline() function, 5-27
imaqGetParticleInfo() function, 4-4
imaqGetPerpendicularLine() function, 5-27
imaqGetPointsOnLine() function, 5-12
imaqGetPolygonArea() function, 5-27
imaqGrab() function, 2-6
imaqGrayMorphology() function, 2-11
IMAQ_HITMISS method, 4-3
imaqInverse() function, 2-10
imaqInverseFFT() function, 2-13
imaqLabel() function, 3-7
imaqLearnCalibrationGrid() function, 6-6
imaqLearnCalibrationPoints() function,
6-5, 6-6
imaqCopyCalibrationInfo() function, 6-10
imaqCopyRing() function, 2-6
imaqCorrectCalibratedImage() function, 4-2
imaqCorrectImage() function, 6-10
imaqCountObjects() function, 5-29
imaqCreateImage() function, 2-2, 2-3
imaqCreateROI() function, 3-6
imaqDisplayImage() function, 2-7
imaqDispose() function, 2-3
imaqEasyAcquire() function, 2-6
imaqEdgeFilter() function, 2-10
imaqEdgeTool() function, 2-9, 5-11
imaqEqualize() function, 2-10
imaqLearnColor() function, 3-9, 3-12, 5-20
imaqLearnColorPattern() function, 5-19,
5-20, 5-25
imaqLearnPattern() function, 5-13, 5-15
imaqLightMeterLine() function, 3-6, 3-7
IMAQ_ERODE method, 4-3
imaqExtractComplextPlane() function, 2-13
imaqExtractFromRing() function, 2-6
imaqFFT() function, 2-12
IMAQ Vision for LabWindows/CVI User Manual
I-4
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Index
imaqLightMeterPoint() function, 3-6, 3-7
imaqLightMeterRect() function, 3-6, 3-7
imaqLineProfile() function, 2-9
imaqLookup() function, 2-10
imaqQuantify() function, 3-7
imaqRake() function, 5-12
imaqReadBarcode() function, 5-28
imaqReadFile() function, 2-6
imaqLowpass() function, 2-10
imaqMaskToRoi() function, 3-7
imaqMatchColor() function, 3-9
imaqMatchColorPattern() function
finding points, 5-19, 5-25
setting parameters, 5-22
testing search algorithm, 5-24
imaqMatchPattern() function
imaqReadMeter() function, 5-27
imaqReadVisionFile() function, 2-7, 6-10
imaqRejectBorder() function, 4-3
imaqReplaceColorPlanes() function, 3-8
imaqReplaceComplextPlane() function, 2-13
imaqResample() function, 6-8
imaqROIProfile() function, 2-9, 5-12
imaqROIToMask() function, 3-4
imaqScale() function, 6-8
angleRanges element, 5-17
corner element, 5-18
finding points, 5-13
minContrast element, 5-17
position element, 5-18
imaqSelectAnnulus() function, 3-6, 5-11
imaqSelectLine() function, 3-6
imaqSelectParticles() function, 4-5
imaqSelectPoint() function, 3-6
imaqSelectRect() function
score element, 5-18
testing search algorithm, 5-18
imaqMathTransform() function, 2-9
imaqMedianFilter() function, 2-11
imaqMorphology() function, 4-3, 4-4
imaqMoveToolWindow() function, 3-5
imaqMoveWindow() function, 2-7
imaqMultiThreshold() function, 4-2
imaqNthOrderFilter() function, 2-10, 2-11
IMAQ_OPEN method, 4-3, 4-4
imaqOverlayArc() function, 5-28
imaqOverlayClosedContour() function, 5-28
imaqOverlayLine() function, 5-28
imaqOverlayMetafile() function, 5-28
imaqOverlayOpenContour() function, 5-28
imaqOverlayOval() function, 5-28
imaqOverlayPoints() function, 5-28
imaqOverlayRect() function, 5-28
imaqOverlayROI() function, 5-28
imaqOverlayText() function, 5-28
imaqParticleFilter() function, 4-4
IMAQ_PCLOSE method, 4-4
IMAQ_POPEN method, 4-3, 4-4
defining ROI, 3-6, 5-8
finding lines or circles, 5-11
making distance measurements, 5-26
imaqSeparation() function, 4-4
imaqSetCurrentTool() function, 3-5
imaqSetSimpleCalibration() function, 6-9
imaqSetupGrab() function, 2-6
imaqSetupRing() function, 2-6
imaqSetupSequence() function, 2-6
imaqSetupToolWindow() function, 3-5
imaqSetWindowPalette() function, 2-7
imaqShowToolWindow() function, 3-5
imaqSimpleEdge() function, 5-11
imaqSizeFilter() function, 4-3
imaqSpoke() function, 5-12
imaqStartAcquisition() function, 2-6
imaqStopAcquisition() function, 2-6
imaqThreshold() function, 4-2
imaqTransformPixelToRealWorld() function,
4-7, 5-26, 6-10
imaqTranspose() function, 2-4
imaqTruncate() function, 2-12
imaqUnwrap() function, 6-8
© National Instruments Corporation
I-5
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Index
imaqWriteVisionFile() function, 5-15,
5-25, 6-10
lowpass frequency filters
attenuation, 2-12
imgInterfaceOpen() function, 2-6
imgSessionOpen() function, 2-6
imgSnap() function, 2-6
instrument reader measurements, 5-27 to 5-28
invalidation of calibration, 6-8
truncation, 2-12
M
machine vision, 5-1 to 5-29
converting pixels coordinates to
real-world coordinates, 5-26
defining region of interest for search area,
5-8 to 5-9
L
learning calibration information, 6-5 to 6-8
choosing learning algorithm, 6-6 to 6-7
choosing ROI, 6-6
interactively, 5-8
programmatically, 5-9
displaying results, 5-28 to 5-29
finding measurement points, 5-9 to 5-25
color location, 5-25
correction table, 6-8
error map, 6-8
invalidation of calibration, 6-8
setting scaling method, 6-8
specifying scaling factors, 6-6
using learning score, 6-6 to 6-8
learning color information, 3-9 to 3-13
choosing color representation
sensitivity, 3-12
color pattern matching, 5-18 to 5-25
edge detection, 5-9 to 5-12
pattern matching, 5-13 to 5-18
locating objects to inspect, 5-2 to 5-7
choosing method for building
coordinate transform (figure), 5-7
edge detection for building
coordinate transform, 5-3 to 5-5
pattern matching for building
coordinate reference, 5-5 to 5-6
making measurements, 5-26 to 5-28
analytic geometry
choosing right color information,
3-9 to 3-10
entire image, 3-10
ignoring learned colors, 3-13
multiple regions in image, 3-11 to 3-12
region in image, 3-10 to 3-11
specifying information to learn,
3-10 to 3-12
measurements, 5-27
distance measurements, 5-26
instrument reader measurements,
5-27 to 5-28
learning template images
color pattern matching, 5-20 to 5-21
pattern matching, 5-15
overview, 5-1 to 5-2
steps for performing (figure), 5-2
Machine Vision function tree, 1-4
masks, for defining regions of interest,
3-6 to 3-7
Line tool (table), 3-2
lines, finding points along edge, 5-9 to 5-11
locating objects to inspect. See machine
vision.
measurement points, finding, 5-9 to 5-25
color location, 5-25
lookup table transformations, 2-9 to 2-10
lowpass filters, 2-10
color pattern matching, 5-18 to 5-25
edge detection, 5-9 to 5-25
IMAQ Vision for LabWindows/CVI User Manual
I-6
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
Index
pattern matching, 5-13 to 5-18
measurement-ready images, acquiring,
2-1 to 2-13
setting matching parameters and
tolerances, 5-16 to 5-17
testing search algorithm on test
images, 5-18
training the algorithm, 5-15
verifying results with ranking
method, 5-18
acquiring or reading images, 2-5 to 2-7
analyzing images, 2-8 to 2-9
attaching calibration information, 2-8
calibrating imaging system, 2-2
creating images, 2-2 to 2-5
displaying images, 2-7
perspective errors, calibrating. See calibration.
pixel coordinates, converting to real-world
coordinates, 4-7, 5-26
improving images, 2-9 to 2-13
complex operations, 2-13
Point tool (table), 3-2
points, finding. See measurement points,
finding.
Polygon tool (table), 3-2
FFT (Fast Fourier Transform),
2-11 to 2-13
filters, 2-10 to 2-11
grayscale morphology, 2-11
lookup tables, 2-9 to 2-10
setting up imaging system, 2-1 to 2-2
R
ranking method for verifying pattern
matching, 5-18
reading images, 2-6 to 2-7
Rectangle tool (table), 3-2
reference coordinate system, defining for
calibration, 6-2 to 6-5
regions of interest, defining, 3-1 to 3-7
for calibration, 6-6
N
NI Developer Zone, A-1
Nth order filters, 2-11
NULL, passing for image mask, 2-5
interactively, 3-1 to 3-6
O
displaying tools palette in separate
window, 3-5
Oval tool (table), 3-2
for machine vision inspection, 5-8
ROI constructor window, 3-3 to 3-4
tools palette functions (table),
3-2 to 3-3
tools palette tools and information
(figure), 3-5
P
Pan tool (table), 3-3
particle measurements, 4-4 to 4-7
pattern matching. See also color pattern
matching.
programmatically, 3-6
for machine vision inspection, 5-9
using masks, 3-6 to 3-7
building coordinate transform, 5-5 to 5-6
finding measurement points, 5-13 to 5-18
defining and creating template
images, 5-13 to 5-15
ROI. See regions of interest, defining.
Rotated Rectangle tool (table), 3-2
defining search area, 5-15 to 5-16
general steps, 5-13
© National Instruments Corporation
I-7
IMAQ Vision for LabWindows/CVI User Manual
Download from Www.Somanuals.com. All Manuals Search And Download.
Index
tools palette functions (table), 3-2 to 3-3
truncation
S
scaling factors, for calibration, 6-6
scaling method, for calibration, 6-8
search contour, finding points along edge,
5-11 to 5-12
highpass, 2-12
lowpass, 2-12
Selection tool (table), 3-2
V
source and destination images, 2-4 to 2-5
statistics. See color statistics; grayscale
statistics.
verifying pattern matching, 5-18
system integration, by National
Instruments, A-1
W
Web support from National Instruments, A-1
Worldwide technical support, A-2
T
technical support resources, A-1 to A-2
template for calibration, defining, 6-2
template images
Z
Zoom tool (table), 3-3
defining
color pattern matching, 5-19 to 5-20
pattern matching, 5-13 to 5-15
training
color pattern matching, 5-20 to 5-21
pattern matching, 5-15
IMAQ Vision for LabWindows/CVI User Manual
I-8
ni.com
Download from Www.Somanuals.com. All Manuals Search And Download.
|