Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation
International Journal of Computer Vision
Outlier Modeling in Image Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Segmentation by Tracking Edge Information over Multiple Frames
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Layer Extraction with a Bayesian Model of Shapes
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Sequential Localisation and Map-Building in Computer Vision and Robotics
SMILE '00 Revised Papers from Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Estimating Piecewise-Smooth Optical Flow with Global Matching and Graduated Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling and Interpretation of Architecture from Several Images
International Journal of Computer Vision
Extracting layers and analyzing their specular properties using epipolar-plane-image analysis
Computer Vision and Image Understanding
A Feature-based Approach for Dense Segmentation and Estimation of Large Disparity Motion
International Journal of Computer Vision
Analysis of Rain and Snow in Frequency Space
International Journal of Computer Vision
Robust subspace clustering by combined use of kNND metric and SVD algorithm
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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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 as a precursor to recovery of stereo disparity data. 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 efficient and effective representation for multimedia applications, results to date have relied on hand segmentation. In contrast, the work described here aims at getting a fully automatic segmentation.