Affiliation:
1. Department of Computer Science Courant Institute of Mathematical Sciences New York University New York, New York 10012
Abstract
In this paper, we describe a technique for geometrically hash ing two-dimensional model objects. Used in conjunction with other methods for recognizing partially obscured and over lapping objects, this technique enables us to recognize over lapping, two-dimensional objects selected from large data bases of model objects without significant performance degradation when the database is enlarged. This technique is based on use of a synthetic attribute of an object, which we will call its footprint. Experimental results from databases of up to 100 objects are presented.
Subject
Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modeling and Simulation,Software
Cited by
100 articles.
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