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Computer Vision and Image Understanding
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International Journal of Computer Vision
Mapping a manifold of perceptual observations
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
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Neural Computation
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International Journal of Computer Vision
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Neural Computation
Separating Style and Content with Bilinear Models
Neural Computation
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
3D People Tracking with Gaussian Process Dynamical Models
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
Nonlinear manifold learning for dynamic shape and dynamic appearance
Computer Vision and Image Understanding
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation
International Journal of Computer Vision
Optimization and Filtering for Human Motion Capture
International Journal of Computer Vision
Physics-Based Person Tracking Using the Anthropomorphic Walker
International Journal of Computer Vision
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Information Sciences: an International Journal
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The problem of separation of style and content is an essential element of visual perception, and is a fundamental mystery of perception. This problem appears extensively in different computer vision applications. The problem we address in this paper is the separation of style and content when the content lies on a low-dimensional nonlinear manifold representing a dynamic object. We show that such a setting appears in many human motion analysis problems. We introduce a framework for learning parameterization of style and content in such settings. Given a set of topologically equivalent manifolds, the Homeomorphic Manifold Analysis (HMA) framework models the variation in their geometries in the space of functions that maps between a topologically-equivalent common representation and each of them. The framework is based on decomposing the style parameters in the space of nonlinear functions that map between a unified embedded representation of the content manifold and style-dependent visual observations. We show the application of the framework in synthesis, recognition, and tracking of certain human motions that follow this setting, such as gait and facial expressions.