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iOS Application Development with OpenCV 3 by Joseph Howse

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Understanding keypoint matching

Previously, in the Understanding detection with cascade classifiers section in Chapter 4, Detecting and Merging Faces of Mammals, we considered the problem of searching for a set of high-contrast features at various positions and various levels of magnification or scale. As we saw, Haar and LBP cascade classifiers solve this problem. Thus, we may say they are scale-invariant (robust to changes in scale). However, we also noted that these solutions are not rotation-invariant (robust to changes in rotation). Why? Consider the individual features. Haar-like features include edges, lines, and dots, which are all symmetric. LBP features are gradients, which may be symmetric, too. A symmetric feature cannot give us a ...

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