Problem with Pattern Matching

Question

Hello,  I am using Vision Development Module 8.5 with VB6.   I need immediate help on coming up with a solution for an auto warranty return for incomplete "Fog Lamp" indicator symbol.  I use a match score minumum of 900, but the match score returns a 932.  I don't see how a score > 900 is returned for so much missing from this symbol.  I use this test in a high production line and am very concerned.  I attached a Vision Assistant 8.5 script and actual image from auto warranty return.  The *.png template used for comparison is also in zip file.
Thanks
Attachments:
PatternMatchScrpt-FogLamp.scr ‏2 KB
Images.zip ‏137 KB

Answer 1

Golden Template does exist in 8.5, and that was my first idea too, however, after trying it, it might not be the best algorithm for it.
Golden Template compare the contour of objects, and they don't produce very good result in the specific image because the symbols are relatively small and not contrasted enough once we look at the luminance plane (i.e. we can't perform a great contour extraction).
What I tried and works decently is the looking at the identification score of the classification algorithm.
The only drawback of this method is that the object needs to be whole (i.e you might need to do a little morphology to close the objects). But the identification score will be quite different on the partial fog lamp.
Another tactic, which is similar to the previous one is to (mis)use the OCR algorithm to train the object and use the character verification function (i.e. define a perfect fog lamp image (character) as the reference character when you train the OCR engine.
The algorithm will them return a verification score which will tell you how far or close the pattern under inspection is from the reference one defined in the OCR file. OCR is based on classification.
After trying that on your image, the verification score is different enough and you could use that for making a decision, but the difference is not as marked as with using the identification score of the classification algorithm.
Both algorithms are based on thresholding the image. One advantage of using the OCR is that you can tweak the min character spacing parameter to adjust the segmentation so that the symbol is considered one full object (as opposed to having to perform morphology to do it if you use the classification algorithm).
As I mentioned, classification seems to give you a better differentiation between the scores.
Both methods allow you to define a Classification file or Character Set File that allow you to test multiple symbols.
I am attaching a couple inspections and files I used to do this quick test.
Hope this helps.
Best regards,
Christophe
Attachments:
Fog Lamp Inspection.zip ‏156 KB