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Spectral Methods for 3-D Motion Segmentation of Sparse Scene-Flow

Diana Mateus 1 Radu Horaud 1 
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : The progress in the acquisition of 3-D data from multicamera set-ups has opened the way to a new way of loking at motion analysis. This paper proposes a solution to the motion segmentation in the context of sparse scene flow. In particular, our interest focuses on the disassociation of motions belonging to different rigid objects, starting from the 3-D trajectories of features lying on their surfaces. We analyze these trajectories and propose a representation suitable for defining robust-pairwise similarity measures between trajectories and handling missing data. The motion segmentation is treated as graph multi-cut problem, and solved with spectral clustering techniques (two algorithms are presented). Experiments are done over simulated and real data in the form of sparse scene-flow; we also evaluate the results on trajectories from motion capture data. A discussion is provided on the results for each algorithm, the parameters and the possible use of these results in motion analysis.
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Submitted on : Tuesday, May 3, 2011 - 9:48:35 AM
Last modification on : Friday, March 11, 2022 - 3:08:02 AM
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Diana Mateus, Radu Horaud. Spectral Methods for 3-D Motion Segmentation of Sparse Scene-Flow. WMVC 2007 - IEEE Workshop on Motion and Video Computing, IEEE, Feb 2007, Austin, United States. pp.14, ⟨10.1109/WMVC.2007.36⟩. ⟨inria-00590241⟩



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