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Clustered Signatures for Modeling and Recognizing 3D Rigid Objects

By H. B. Darb, M. R. Ito and J. Little

Abstract

Abstract — This paper describes a probabilistic method for three-dimensional object recognition using a shared pool of surface signatures. This technique uses flatness, orientation, and convexity signatures that encode the surface of a free-form object into three discriminative vectors, and then creates a shared pool of data by clustering the signatures using a distance function. This method applies the Bayes’s rule for recognition process, and it is extensible to a large collection of three-dimensional objects. Keywords—Object recognition, modeling, classification, computer vision. I

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.193.1085
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