An Efficiently Computable Metric for Comparing Polygonal Shapes
CORNELL UNIV ITHACA NY DEPT OF COMPUTER SCIENCE
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Model-based recognition is concerned with comparing a shape A, which is stored as a model for some particular object, with a shape B, which is found to exist in an image. If A and B are close to being the same shape, then a vision system should report a match and return a measure of how good that match is. To be useful this measure should satisfy a number of properties, including 1 it should be a metric, 2 it should be invariant under translation, rotation, and change-of-scale, 3 it should be reasonably easy to compute, and 4 it should match our intuition i.e., answers should be similar to those that a person might give. We develop a method for comparing polygons that has these properties. The method works for both convex and nonconvex polygons and runs in time Omn log mn where m is the number of vertices in one polygon and n is the number of vertices in the other. Some examples are presented that show the method produces answers that are intuitively reasonable.