Matching shapes
Belongie, S.; Malik, J.; Puzicha, J.
Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on
Volume 1, Issue , 2001 Page(s):454 - 461 vol.1
Digital Object Identifier 10.1109/ICCV.2001.937552
Summary:We present a novel approach to measuring similarity between shapes
and exploit it for object recognition. In our framework, the measurement
of similarity is preceded by (1) solving for correspondences between
points on the two shapes, (2) using the correspondences to estimate an
aligning transform. In order to solve the correspondence problem, we
attach a descriptor, the shape context, to each point. The shape context
at a reference point captures the distribution of the remaining points
relative to it, thus offering a globally discriminative
characterization. Corresponding points on two similar shapes will have
similar shape contexts, enabling us to solve for correspondences as an
optimal assignment problem. Given the point correspondences, we estimate
the transformation that best aligns the two shapes; regularized
thin-plate splines provide a flexible class of transformation maps for
this purpose. Dis-similarity between two shapes is computed as a sum of
matching errors between corresponding points, together with a term
measuring the magnitude of the aligning transform. We treat recognition
in a nearest-neighbor classification framework. Results are presented
for silhouettes, trademarks, handwritten digits and the COIL dataset
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