Model-Based Multiple View Reconstruction of People
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Shape-From-Silhouette Across Time Part I: Theory and Algorithms
International Journal of Computer Vision
Invariant High Level Reeb Graphs of 3D Polygonal Meshes
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Performance capture from sparse multi-view video
ACM SIGGRAPH 2008 papers
Shape Similarity for 3D Video Sequences of People
International Journal of Computer Vision
Human action recognition using multiple views: a comparative perspective on recent developments
J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Topology Dictionary for 3D Video Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
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3D video retrieval is a challenging problem lying at the heart of many primary research areas in computer graphics and computer vision applications. In this paper, we present a new 3D human shape matching and motion retrieval framework. Our approach is formulated using Extremal Human Curve (EHC) descriptor extracted from the body surface and a local motion retrieval achieved after motion segmentation. Matching is performed by an efficient method which takes advantage of a compact EHC representation in open curve Shape Space and an elastic distance measure. Moreover, local 3D video retrieval is performed by dynamic time warping (DTW) algorithm in the feature space vectors. Experiments on both synthetic and real 3D human video sequences show that our approach provides an accurate shape similarity in video compared to the best state-of-the-art approaches. Finally, results on motion retrieval are promising and show the potential of this approach.