Disparity contour grouping for multi-object segmentation in dynamically textured scenes

A fast multi-object segmentation algorithm based on disparity contour grouping is described. It segments multiple objects at a wide range of depths from backgrounds of known geometry in a manner insensitive to changing lighting and the dynamic texture of, for example, display surfaces. Not relying on stereo reconstruction or prior knowledge of foreground objects, it is fast enough on commodity hardware for some real-time applications. Experimental results demonstrate its ability to extract object contour from a complex scene and distinguish multiple objects even when they are close together or partially occluded.

[1]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[2]  Li Zhang,et al.  Rapid shape acquisition using color structured light and multi-pass dynamic programming , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[3]  Andrew Blake,et al.  Bi-layer segmentation of binocular stereo video , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Carlo Tomasi,et al.  Surfaces with occlusions from layered stereo , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Harpreet S. Sawhney,et al.  Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding , 1995, Proceedings of IEEE International Conference on Computer Vision.

[6]  V. Leitáo,et al.  Computer Graphics: Principles and Practice , 1995 .

[7]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Edward H. Adelson,et al.  Layered representation for motion analysis , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Andrew Blake,et al.  A Probabilistic Background Model for Tracking , 2000, ECCV.

[10]  Nanning Zheng,et al.  Stereo Matching Using Belief Propagation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Takeo Kanade,et al.  Constructing virtual worlds using dense stereo , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[12]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  James H. Elder,et al.  Contour Grouping with Prior Models , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Michael J. Black,et al.  Mixture models for optical flow computation , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Vladimir Kolmogorov,et al.  Multi-camera Scene Reconstruction via Graph Cuts , 2002, ECCV.

[17]  A. Verri,et al.  A compact algorithm for rectification of stereo pairs , 2000 .

[18]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Edward H. Adelson,et al.  A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  J. Elder,et al.  Ecological statistics of Gestalt laws for the perceptual organization of contours. , 2002, Journal of vision.

[21]  Richard Szeliski,et al.  An integrated Bayesian approach to layer extraction from image sequences , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[22]  Aaron F. Bobick,et al.  Fast Lighting Independent Background Subtraction , 2004, International Journal of Computer Vision.

[23]  Wei Sun,et al.  An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques , 2006, Machine Vision and Applications.