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Fast Pose Estimation with Parameter-Sensitive Hashing
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
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ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Kinematic jump processes for monocular 3D human tracking
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Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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This paper presents a patch-based approach for pose estimation from single images using a kernelized density voting scheme. We introduce a boosting-like algorithm that models the density using a mixture of weighted 'weak' estimators. The 'weak' density estimators and corresponding weights are learned iteratively from a training set, providing an efficient method for feature selection. Given a query image, voting is performed by reference patches similar in appearance to query image patches. Locality in the voting scheme allows us to handle occlusions and reduces the size of the training set required to cover the space of possible poses and appearance. Finally, the pose is estimated as the dominant mode in the density. Multimodality can be handled by looking at multiple dominant modes. Experiments carried out on face and articulated body pose databases show that our patch-based pose estimation algorithm generalizes well to unseen examples, is robust to occlusions and provides accurate pose estimation.