Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
An Integrated Bayesian Approach to Layer Extraction from Image Sequences

P. H. S. Torr, Richard Szeliski, and P. Anandan

Abstract

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 compute layered descriptions of 2D image motion, our work leads to a 3D description of the scene. We focus on the key problem of automatically segmenting the scene into layers based on stereo disparity data from multiple images. The prior assumptions about the scene are formulated within a Bayesian decision making framework, and are then used to automatically determine the number of layers and the assignment of individual pixels to layers. Although using a collection of 3D layers has been previously proposed as an e±cient and e®ective representation for multimedia applications, results to date have relied on hand segmentation. In contrast, the work described here aims at getting the best automatic segmentation that is possible based on disparity and color data alone.

Details

Publication typeInproceedings
Published inSeventh International Conference on Computer Vision (ICCV'99)
Pages983-990
AddressKerkyra, Greece
PublisherIEEE Computer Society
> Publications > An Integrated Bayesian Approach to Layer Extraction from Image Sequences