Triadic distance models: axiomatization and least squares representation
Journal of Mathematical Psychology
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Inferring 3D Structure with a Statistical Image-Based Shape Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
M^3: Marker-Free Model Reconstruction and Motion Tracking from 3D Voxel Data
PG '04 Proceedings of the Computer Graphics and Applications, 12th Pacific Conference
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Segmentation and Probabilistic Registration of Articulated Body Models
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Finding articulated body in time-series volume data
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Learning nonlinear manifolds from time series
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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In this paper we present a novel tool for body-part segmentation and tracking in the context of multiple camera systems. Our goal is to produce robust motion cues over time sequences, as required by human motion analysis applications. Given time sequences of 3D body shapes, body-parts are consistently identified over time without any supervision or a priori knowledge. The approach first maps shape representations of a moving body to an embedding space using locally linear embedding. While this map is updated at each time step, the shape of the embedded body remains stable. Robust clustering of body parts can then be performed in the embedding space by k-wise clustering, and temporal consistency is achieved by propagation of cluster centroids. The contribution with respect to methods proposed in the literature is a totally unsupervised spectral approach that takes advantage of temporal correlation to consistently segment body-parts over time. Comparisons on real data are run with direct segmentation in 3D by EM clustering and ISOMAP-based clustering: the way different approaches cope with topology transitions is discussed.