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    <p>In general you have the following steps were changes could affect the system performance:</p> <ol> <li>Detector and its configuration</li> <li>Descriptor and its configuration</li> <li>Matcher and its configuration</li> <li>RANSAC parameters (or whatever you're using)</li> </ol> <p>You could try <a href="http://docs.opencv.org/modules/features2d/doc/feature_detection_and_description.html" rel="nofollow">MSER (maximally stable extremal region)</a> as an detector. This detector is known not to find as much keypoints as other detectors like SIFT/SURF. But they are very stable and unique. But I would try a different descriptor for matching. Perhaps <a href="http://docs.opencv.org/modules/features2d/doc/feature_detection_and_description.html#freak" rel="nofollow">FREAK (Fast Retina Keypoint)</a>. </p> <p>The Filtering with the RANSAC-algorithm should do but you can always tweak by setting a distance you like. If RANSAC doesn't work well then try switching the default threshold until it seems to work better.</p> <p>Another thing you can change is the matcher. FLANN is very good but only approximates the results and receives great performs when searching in high-dimensional spaces. But if performance doesn't concern you then try a BruteForceMatcher with euclidean or Mahalanobis distance.</p> <p><strong>[EDIT-1]:</strong> *<em>Now I'm trying to give you additional could-help-your-suggestions =) *</em> Kampel et al. describe in their article "Image-Based Retrieval and Identification of Ancient Coins" some additional approaches to give an acient coin a shape-descriptor to enhance the describing features of items.</p> <p>Another problem could be RANSAC which is a very restrictive approach to eliminate outliers. The method <code>Mat findHomography(InputArray srcPoints, InputArray dstPoints, int method=0, double ransacReprojThreshold=3, OutputArray mask=noArray() )</code> has additional parameters (<a href="http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html?highlight=findhomograp#findhomography" rel="nofollow">click this link</a>). You could use all points, RANSAC or a Least-Median robust method.</p> <p>Or you try to make a robust calculation what distance might be a good threshold to retrieve good matches. The standard tutorial uses two times the minimal found distance which is a start but not very robust cause it depends on the distance of the <em>current</em> matches.</p> <p>There are several other descriptors/detectors which are not included in the standard OpenCV. Lindbjerg describes in his paper "Finding the Best Feature Detector-Descriptor Combination" that MSER works finde with a DAISY-Descriptor. Or you could try <a href="http://www6.in.tum.de/Main/ResearchAgast" rel="nofollow">AGAST</a> or <a href="http://www.asl.ethz.ch/people/lestefan/personal/BRISK" rel="nofollow">BRISK</a>. One real big drawback is that they are all not included in the standard OpenCV distribution. There are some implementations but you have to integrate them which could mean a lot of work.</p>
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    1. POPattern Matching - Find reference object in second image [OpenCV?]
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    1. COI have tried MSER and FREAK but it seems they are not very good for the images or objects I am analyzing (detailed, kinda noisy, rough). I could not get a good match even if the coin is the same. I have also tweaked the RANSAC threshold until it worked for ORB, but that didn't help with detecting different coins at all.
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    2. COI added some additional suggestions for you. [EDIT-1]
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    3. CODo you mean Malanobis distance? I couldn't find a mahagoni that wasn't a misplet wood.
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