Template Matching Subpixel Accuracy

Question

I use template matching to detect a specific pattern in image.The shift determined is very shaky. Currently I apply it to R,G,B channel separately and average the result to obtain float values.Please, suggest how to obtain subpixel accuracy. I was planning to resize the image and then return the data in original scale, please suggest any other better method.

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I use the code mentioned on Opencv site " http://docs.opencv.org/2.4/doc/tutorials/imgproc/histograms/template_matching/template_matching.html "

Answer 1

It's a good thing to start with pixel accuracy before moving to subpixel accuracy. Checking the whole image at subpixel accuracy would be way to expensive.

An easy solution could be to have 4 versions of your template. Besides the base one, have one that's shifted 1/2 pixel left, another that's shifted 1/2 pixel down, and finally one that's shifted 1/2 pixel in both directions. When you have a match at {x,y} , check the neighborhood to see if the half-shifted templates are a better match.

The benefit of this method is that you only need to shift the small template, and it can be done up front.

Having said that, it seems you're tracking an object position over time. It may be worthwhiel to low-pass filter that position.

Answer 2

I believe the underlying issue is that minMaxLoc has only pixel accuracy. You could try out a subpixel-accurate patch http://www.longrange.net/Temp/CV_SubPix.cpp from the discussion here: http://answers.opencv.org/question/29665/getting-subpixel-with-matchtemplate/ .

As a quick and dirty experiment if a sub-pixel accurate minMaxLoc would resolve your issue, you can scale up the template matching result image (by a factor of 4, for instance) with cubic interpolation INTER_CUBIC http://docs.opencv.org/2.4/modules/imgproc/doc/geometric_transformations.html#resize and apply minMaxLoc on it. (Contrary to linear interpolation, cubic interpolation does move maxima.)

Apart from this, you can always apply Gaussian blur to both input images and template matching results to reduce high-frequency noise and suppress local maxima.

I would first try out the quick experiment. If it helps, you can integrate the minMaxLogSubPix implementation, but that will take longer.