What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
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CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Interactive Graph Cut Based Segmentation with Shape Priors
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Full Body Tracking from Multiple Views Using Stochastic Sampling
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Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
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A graphical model framework for coupling MRFs and deformable models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Graph Cuts and Efficient N-D Image Segmentation
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Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
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Computer Vision and Image Understanding
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts
International Journal of Computer Vision
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
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VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Analyzing Gait Using a Time-of-Flight Camera
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
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Dyn3D '09 Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging
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Optimization and Filtering for Human Motion Capture
International Journal of Computer Vision
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International Journal of Computer Vision
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
SIAM Journal on Imaging Sciences
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EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
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International Journal of Computer Vision
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ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
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ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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ACM Transactions on Graphics (TOG)
Object class detection: A survey
ACM Computing Surveys (CSUR)
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We present a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other related state of the art techniques which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Normally, when optimizing for pose, it is traditional to use some fixed set of features, e.g. edges or chamfer maps. In contrast, our novel approach consists of optimizing a cost function based on a Markov Random Field (MRF). This has the advantage that we can use all the information in the image: edges, background and foreground appearances, as well as the prior information on the shape and pose of the subject and combine them in a Bayesian framework. Previously, optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Note that although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any specified rigid, deformable or articulated object.