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Shape Matching and Object Recognition Using Shape Contexts

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361
CZD44
12-20-2014
11:08 AM ET (US)
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hadloven
12-16-2014
08:18 PM ET (US)
Shape context is a feature descriptor used in object recognition. Serge Belongie and Jitendra Malik proposed the term in their paper "Matching with Shape Contexts" in 2000. The shape context is intended to be a way of describing shapes that allows for measuring shape similarity and the recovering of point correspondences.[1] The basic idea is to pick n points on the contours of a shape. For each point pi on the shape, consider the n − 1 vectors obtained by connecting pi to all other points. The set of all these vectors is a rich description of the shape localized at that point but is far too detailed. The key idea is that the distribution over relative positions is a robust, compact, and highly discriminative descriptor. So, for the point pi, the coarse histogram of the relative coordinates of the remaining n − 1 points,
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12-03-2014
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11-10-2014
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11-06-2014
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