An efficiently computable metric for comparing polygonal shapes

Esther M. Arkin, L. Paul Chew, David P. Huttenlocher, Klara Kedem, Joseph S.B. Mitchell

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Scopus citations

Abstract

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 0(mn log mn) where m is the number of vertices in one polygon and n is the number of vertices in the other. We also present some examples to show that the method produces answers that are intuitively reasonable.

Original languageEnglish
Title of host publicationProceedings of the 1st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1990
PublisherAssociation for Computing Machinery
Pages129-137
Number of pages9
ISBN (Electronic)0898712513
StatePublished - 1 Jan 1990
Externally publishedYes
Event1st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1990 - San Francisco, United States
Duration: 22 Jan 199024 Jan 1990

Publication series

NameProceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms

Conference

Conference1st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1990
Country/TerritoryUnited States
CitySan Francisco
Period22/01/9024/01/90

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