Hybrid parametric case-based approach to object recognition using Bayes decision theory

David H. Haussler; Vincent Mirelli

doi:10.1117/12.234247

4 March 1996 Hybrid parametric case-based approach to object recognition using Bayes decision theory

David H. Haussler, Vincent Mirelli

Proceedings Volume 2664, Applications of Artificial Neural Networks in Image Processing; (1996) https://doi.org/10.1117/12.234247
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States

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.

Citation Download Citation

David H. Haussler and Vincent Mirelli "Hybrid parametric case-based approach to object recognition using Bayes decision theory", Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); https://doi.org/10.1117/12.234247

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