Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Learning the Statistics of People in Images and Video
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Pedestrian Models for Silhouette Refinement
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive Graph Cut Based Segmentation with Shape Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Progressive Refinement of Raster Images
IEEE Transactions on Computers
People tracking and segmentation using spatiotemporal shape constraints
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
POSECUT: simultaneous segmentation and 3D pose estimation of humans using dynamic graph-cuts
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking
IEEE Transactions on Image Processing
Shape prior embedded geodesic distance transform for image segmentation
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
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We design an effective shape prior embedded human silhouettes extraction algorithm. Human silhouette extraction is found challenging because of articulated structures, pose variations, and background clutters. Many segmentation algorithms, including the Min-Cut algorithm, meet difficulties in human silhouette extraction. We aim at improving the performance of the Min-Cut algorithm by embedding shape prior knowledge. Unfortunately, seeking shape priors automatically is not trivial especially for human silhouettes. In this work, we present a shape sequence matching method that searches for the best path in spatial-temporal domain. The path contains shape priors of human silhouettes that can improve the segmentation. Matching shape sequences in spatial-temporal domain is advantageous over finding shape priors by matching shape templates with a single likelihood frame because errors can be avoided by searching for the global optimization in the domain. However, the matching in spatial-temporal domain is computationally intensive, which makes many shape matching methods impractical. We propose a novel shape matching approach that has low computational complexity independent of the number of shape templates. In addition, we investigate on how to make use of shape priors in a more adequate way. Embedding shape priors into the Min-Cut algorithm based on distances from shape templates is lacking because Euclidean distances cannot represent shape knowledge in a fully appropriate way. We embed distance and orientation information of shape priors simultaneously into the Min-Cut algorithm. Experimental results demonstrate that our algorithm is efficient and practical. Compared with previous works, our silhouettes extraction system produces better segmentation results.