Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color image processing and applications
Color image processing and applications
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Intensity and Motion for Incremental Segmentation and Tracking Over Long Image Sequences
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Segmentation and Tracking of Interacting Human Body Parts under Occlusion and Shadowing
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Bi-Layer Segmentation of Binocular Stereo Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning Layered Motion Segmentation of Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Motion layer extraction in the presence of occlusion using graph cut
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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In this paper we propose a video segmentation algorithm that in the final delineation of the object employs the graph-cut. A partitioning of the image based on pairwise region comparison is done at the beginning of each frame. A set of keypoints is tracked over time via optical flow to extract regions, which are likely to be parts of the object of interest. The tracked keypoints contribute towards better temporal coherence of the object segmentation. A probabilistic occupancy map of the object is extracted using such initial object segmentation and a probabilistic shape model. The map is utilized in a classifier that operates both on pixels and regions. The aim of the classifier is to extract a trimap consisting of foreground, background and unknown areas. The trimap is employed by graph-cut. The outcome of the graph-cut is used in on-line learning of the shape model. The performance of the algorithm is demonstrated on freely available test sequences.