Share Email Print

Proceedings Paper

Hybrid parametric case-based approach to object recognition using Bayes decision theory
Author(s): David H. Haussler; Vincent Mirelli
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

We consider the problem of recognition of rigid, manufactured objects, each from a predefined set of possible object classes, from their images. We describe a parametric statistical approach to this problem that is a hybrid between statistical modeling using Bayes decision theory with a generative model of images and a case-based approach. Our method is a variant of the Gibbs sampling method, commonly used to compute posterior probabilities in complex statistical models, but unlike standard Gibbs sampling methods, our method is based directly on analysis of a library of previously analyzed images. We also propose a simple gradient descent method to optimize the parameters of the models to maximize effective object recognition.

Paper Details

Date Published: 4 March 1996
PDF: 11 pages
Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); doi: 10.1117/12.234247
Show Author Affiliations
David H. Haussler, Univ. of California/Santa Cruz (United States)
Vincent Mirelli, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 2664:
Applications of Artificial Neural Networks in Image Processing
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

© SPIE. Terms of Use
Back to Top