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If I add normalization after the matching with Core.normalize(result, result, 0, 1, Core.NORM_MINMAX, -1, new Mat())

Localizing the best match with minMaxLoc

Imgproc.matchTemplate(img, templ, result, match_method) Mat result = new Mat(result_rows, result_cols, CvType.CV_32FC1) Int result_rows = img.rows() - templ.rows() + 1 Mat templ = Highgui.imread(templateFile) Here is some more code: Mat img = Highgui.imread(inFile) In the end I would like to know when to use which match method for what kind of image to get the best results. I would be glad if someone can explain me the difference between the matching methods or give a link to already an existing page where these methods are being discussed, on opencv docs/tutorials there are only the formulas available but no detailed explanation. A little bit annoying, I would be glad for your suggestions and comments for improvement because TM_CCOEFF_NORMED gives always the first searched square/rectangle as best match with score 1.0, this cant be correct, on the other hand I also tried TM_CCORR_NORMED and TM_SQDIFF_NORMED they gave different match scores, this is promising but still TM_CCORR_NORMED gave good matching score which is still unexpected from my side. MinMaxLocResult mmr = Core.minMaxLoc(result) ġ.0 is the best match value, which means the image is %100 identical, but it isn't and on other reference images I get other values, so the program is working but only with this specific reference image I'm getting 1.0 also no matter what in file(source image) I give, it always finds the match on the first search upper left corner.

Until now I have always gotten reasonable results withc matching score, Im using "Imgproc.TM_CCOEFF_NORMED" method for template matching below are the results.