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copied!<p>An alternative approach would be to extract features (keypoints) using the <a href="http://en.wikipedia.org/wiki/Scale-invariant_feature_transform" rel="noreferrer">scale-invariant feature transform</a> (SIFT) or <a href="http://en.wikipedia.org/wiki/SURF" rel="noreferrer">Speeded Up Robust Features</a> (SURF).</p> <p>It is implemented in <a href="http://en.wikipedia.org/wiki/OpenCV" rel="noreferrer">OpenCV</a> 2.3.1.</p> <p>You can find a nice code example using features in <em><a href="http://docs.opencv.org/2.4/doc/tutorials/features2d/feature_homography/feature_homography.html" rel="noreferrer">Features2D + Homography to find a known object</a></em></p> <p>Both algorithms are invariant to scaling and rotation. Since they work with features, you can also handle <a href="http://en.wikipedia.org/wiki/Ambient_occlusion" rel="noreferrer">occlusion</a> (as long as enough keypoints are visible).</p> <p><img src="https://i.stack.imgur.com/kF63R.jpg" alt="Enter image description here"></p> <p>Image source: tutorial example</p> <p>The processing takes a few hundred ms for SIFT, SURF is bit faster, but it not suitable for real-time applications. ORB uses FAST which is weaker regarding rotation invariance.</p> <h3>The original papers</h3> <ul> <li><a href="http://www.vision.ee.ethz.ch/~surf/eccv06.pdf" rel="noreferrer">SURF: Speeded Up Robust Features</a></li> <li><a href="http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf" rel="noreferrer">Distinctive Image Features from Scale-Invariant Keypoints</a></li> <li><a href="http://www.willowgarage.com/sites/default/files/orb_final.pdf" rel="noreferrer">ORB: an efficient alternative to SIFT or SURF</a></li> </ul>

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