Group-valued regularization framework for motion segmentation of dynamic non-rigid shapes
Publication
, Conference
Rosman, G; Bronstein, MM; Bronstein, AM; Wolf, A; Kimmel, R
Published in: Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
January 16, 2012
Understanding of articulated shape motion plays an important role in many applications in the mechanical engineering, movie industry, graphics, and vision communities. In this paper, we study motion-based segmentation of articulated 3D shapes into rigid parts. We pose the problem as finding a group-valued map between the shapes describing the motion, forcing it to favor piecewise rigid motions. Our computation follows the spirit of the Ambrosio-Tortorelli scheme for Mumford-Shah segmentation, with a diffusion component suited for the group nature of the motion model. Experimental results demonstrate the effectiveness of the proposed method in non-rigid motion segmentation. © 2012 Springer-Verlag.
Duke Scholars
Published In
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
DOI
EISSN
1611-3349
ISSN
0302-9743
Publication Date
January 16, 2012
Volume
6667 LNCS
Start / End Page
725 / 736
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences
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MLA
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Rosman, G., Bronstein, M. M., Bronstein, A. M., Wolf, A., & Kimmel, R. (2012). Group-valued regularization framework for motion segmentation of dynamic non-rigid shapes. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics (Vol. 6667 LNCS, pp. 725–736). https://doi.org/10.1007/978-3-642-24785-9_61
Rosman, G., M. M. Bronstein, A. M. Bronstein, A. Wolf, and R. Kimmel. “Group-valued regularization framework for motion segmentation of dynamic non-rigid shapes.” In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6667 LNCS:725–36, 2012. https://doi.org/10.1007/978-3-642-24785-9_61.
Rosman G, Bronstein MM, Bronstein AM, Wolf A, Kimmel R. Group-valued regularization framework for motion segmentation of dynamic non-rigid shapes. In: Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. 2012. p. 725–36.
Rosman, G., et al. “Group-valued regularization framework for motion segmentation of dynamic non-rigid shapes.” Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 6667 LNCS, 2012, pp. 725–36. Scopus, doi:10.1007/978-3-642-24785-9_61.
Rosman G, Bronstein MM, Bronstein AM, Wolf A, Kimmel R. Group-valued regularization framework for motion segmentation of dynamic non-rigid shapes. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. 2012. p. 725–736.
Published In
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
DOI
EISSN
1611-3349
ISSN
0302-9743
Publication Date
January 16, 2012
Volume
6667 LNCS
Start / End Page
725 / 736
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences