Method and system for detecting a flaw in a sample image
First Claim
1. A method for detecting a flaw in a sample image of an object in a machine vision system including a camera and a computer, the method comprising the steps of:
- a) creating a template image of the object;
b) registering the template image and the sample image;
c) subtracting the template image from the sample image to provide an error image of the sample image after performing step b);
d) analyzing the error image to identify at least one region of error of the sample image;
e) creating an individual final sample sub-image of the sample image at each identified region of error;
f) creating an individual final template sub-image of the template image at each identified region of error of the sample image;
g) registering each individual final template sub-image and each corresponding individual final sample sub-image;
h) subtracting each final template sub-image from each corresponding final sample sub-image to provide a final error image for each final sample sub-image;
i) analyzing the final error image for each final sample sub-image to identify a final sample sub-image region of error for each final sample sub-image; and
i) identifying each final sample sub-image region of error as the flaw.
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Abstract
A method and system are described for use at a machine vision station to correctly identify flaws within a sample image by means of flexible template image comparison where the template image and a sample image suffer from a relative geometrical distortion. The method and system use the following iterative approach: 1) generate the template image; 2) register the sample image to the template image; 3) undertake an absolute image subtraction process; 4) identify all regions of image difference; 5) create sub-images of both the template and the sample images in the region of detected error; 6) repeat the registration step 2) using the sub-images; 7) undertake the absolute subtraction process on each of the sub-images; and 8) either identify the resulting differences as flaws or go to step 5) using even smaller sub-images. The invention has particular utility in the screen printing industry as well as other industries.
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Citations
14 Claims
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1. A method for detecting a flaw in a sample image of an object in a machine vision system including a camera and a computer, the method comprising the steps of:
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a) creating a template image of the object;
b) registering the template image and the sample image;
c) subtracting the template image from the sample image to provide an error image of the sample image after performing step b);
d) analyzing the error image to identify at least one region of error of the sample image;
e) creating an individual final sample sub-image of the sample image at each identified region of error;
f) creating an individual final template sub-image of the template image at each identified region of error of the sample image;
g) registering each individual final template sub-image and each corresponding individual final sample sub-image;
h) subtracting each final template sub-image from each corresponding final sample sub-image to provide a final error image for each final sample sub-image;
i) analyzing the final error image for each final sample sub-image to identify a final sample sub-image region of error for each final sample sub-image; and
i) identifying each final sample sub-image region of error as the flaw. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system for detecting a flaw in a sample image of an object at a vision station including a camera, the system comprising:
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first means for creating a template image of the object;
second means for registering the template image and the sample image;
third means for subtracting the registered template image from the sample image to provide an error image of the sample image;
fourth means for analyzing the error image to identify at least one region of error of the sample image;
fifth means for creating an individual final sample sub-image and an individual template sub-image in each identified region of error, the second means registering each final sub-image of the template image and the sample image, the third means subtracting each final registered sub-image of the template image and the sample image to provide a final error image for each final sample sub-image and the fourth means analyzing each final error image of each final sample sub-image to identify a final sample sub-image region of error for each final sample sub-image; and
sixth means for identifying each final sample sub-image region of error as the flaw. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A method for detecting a flaw in a sample image of a geometrically distortable object in a machine vision system including a camera and a computer, the method comprising the steps of:
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a) presenting a geometrically distortable object for flaw detection;
b) creating a template image of the object wherein substantially no effectively constant geometric relationship exists between the template image and the sample image;
c) registering the template image and the sample image;
d) subtracting the template image from the sample image to provide an error image of the sample image after performing step c);
e) analyzing the error image to identify a region of error of the sample image;
f) creating a final sub-image of both the template image and the sample image in the identified region of error;
g) repeating steps c) through e) using the final sub-images to identify a final region of error; and
h) identifying the identified final region of error as the flaw.
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14. A system for detecting a flaw in a sample image of a geometrically distortable object at a vision station including a camera, the system comprising:
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first means for creating a template image of the object wherein substantially no effectively constant geometric relationship exists between the template image and the sample image;
second means for registering the template image and the sample image;
third means for subtracting the registered template image from the sample image to provide an error image of the sample image;
fourth means for analyzing the error image to identify at least one region of error of the sample image;
fifth means for creating a final sub-image of both the template image and the sample image in the identified region of error, the second means registering the final sub-images of the template image and the sample image, the third means subtracting the final registered sub-image of the template image and the sample image to provide a final error image and the fourth means analyzing the final error image to identify a final region of error; and
sixth means for identifying the final region of error as the flaw.
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Specification