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    <p>Can the screenshot or icon be transformed (scaled, rotated, skewed ...)? There are quite a few methods on top of my head that could possibly help you:</p> <ul> <li><strong>Simple euclidean distance</strong> as mentioned by @carlosdc (doesn't work with transformed images and you need a threshold).</li> <li><strong><a href="http://en.wikipedia.org/wiki/Cross-correlation" rel="noreferrer">(Normalized) Cross Correlation</a></strong> - a simple metrics which you can use for comparison of image areas. It's more robust than the simple euclidean distance but doesn't work on transformed images and you will again need a threshold.</li> <li><strong>Histogram comparison</strong> - if you use normalized histograms, this method works well and is not affected by affine transforms. The problem is determining the correct threshold. It is also very sensitive to color changes (brightness, contrast etc.). You can combine it with the previous two.</li> <li><strong>Detectors of salient points/areas</strong> - such as <a href="http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions" rel="noreferrer">MSER (Maximally Stable Extremal Regions)</a>, <a href="http://en.wikipedia.org/wiki/SURF" rel="noreferrer">SURF</a> or <a href="http://en.wikipedia.org/wiki/Scale-invariant_feature_transform" rel="noreferrer">SIFT</a>. These are very robust algorithms and they might be too complicated for your simple task. Good thing is that you do not have to have an exact area with only one icon, these detectors are powerful enough to find the right match. A nice evaluation of these methods is in this paper: <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.157.4126&rep=rep1&type=pdf" rel="noreferrer">Local invariant feature detectors: a survey</a>.</li> </ul> <p>Most of these are already implemented in OpenCV - see for example the cvMatchTemplate method (uses histogram matching): <a href="http://dasl.mem.drexel.edu/~noahKuntz/openCVTut6.html" rel="noreferrer">http://dasl.mem.drexel.edu/~noahKuntz/openCVTut6.html</a>. The salient point/area detectors are also available - see <a href="http://opencv.willowgarage.com/documentation/cpp/feature_detection.html" rel="noreferrer">OpenCV Feature Detection</a>.</p>
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    1. USKarel Petranek
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    1. COIt can be scaled or moved slightly. Also the background of the icon will be different. I tried histogram comparison but I got many false positives. I also tried euclidean distance but that also gives too many false positives (but maybe I can make that a bit better some handling for the alpha value in the icon). I will try that a bit further, otherwise I will check out MSER, SURF or SIFT.
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    2. COAnother idea - wouldn't it work if you used histogram comparison of the images after applying a sobel operator? That would only compare similarity of edges. Might or might not work, depending on how "edgy" the background is.
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      1. USKarel Petranek
 

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