The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.03 - March (2001 vol.23)
pp: 297-303
ABSTRACT
<p><b>Abstract</b>—This paper describes a Bayesian approach for modeling 3D scenes as a 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.</p>
INDEX TERMS
Layer extraction, segmentation, stereo matching, motion estimation.
CITATION
Richard Szeliski, Philip H.S. Torr, "An Integrated Bayesian Approach to Layer Extraction from Image Sequences", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.23, no. 3, pp. 297-303, March 2001, doi:10.1109/34.910882
28 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool