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Abstract: Three dimensional vision relies on the ability to generalize from known views of an object to novel views. Of particular interest is the ability of human observers to generalize from a single view of a previously unseen object to novel views. This paper describes a method for achieving single view generalization by modeling conditional densities of the form P (S|B, B # ) or P (B # S) and applying them in a Bayesian decision theoretic framework, where B and B # are two views and S is a... (Update)
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BibTeX entry: (Update)
@misc{ breuel-bayesian,
author = "Thomas M. Breuel",
title = "A Bayesian Approach to Learning Single View Generalization in 3D Object
Recognition",
url = "citeseer.ist.psu.edu/article/breuel03bayesian.html" }
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Documents on the same site (http://tmb.voxel.net/papers/other/): More
View Based Methods can achieve Bayes-Optimal 3D Recognition - Breuel (2003) (Correct)
Learning View Generalization Functions - Thomas Breuel Parc (2003) (Correct)
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