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Chapter 5
Machine Vision
©
National Instruments Corporation
5-13
IMAQ Vision for LabWindows/CVI User Manual
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.