An integrated Bayesian approach to layer extraction from imagesequences
Torr, P.H.S.; Szeliski, R.; Anandan, P.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 23, Issue 3, Mar 2001 Page(s):297 - 303
Digital Object Identifier   10.1109/34.910882
Summary:This paper describes a Bayesian approach for modeling 3D scenes as collection of approximately planar layers that are arbitrarily positioned and oriented in the scene. In contrast to much of the previous work on layer-based motion modeling, which computes layered descriptions of 2D image motion, our work leads to a 3D description of the scene. There are two contributions within the paper. The first is to formulate the prior assumptions about the layers and scene within a Bayesian decision making framework which is used to automatically determine the number of layers and the assignment of individual pixels to layers. The second is algorithmic. In order to achieve the optimization, a Bayesian version of RANSAC is developed with which to initialize the segmentation. Then, a generalized expectation maximization method is used to find the MAP solution

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