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Volumn , Issue , 2007, Pages

Segmenting motions of different types by unsupervised manifold clustering

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; DATA REDUCTION; DATABASE SYSTEMS; PROBLEM SOLVING; VECTORS;

EID: 34948906262     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2007.383235     Document Type: Conference Paper
Times cited : (182)

References (23)
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    • M. Polito and P. Perona. Grouping and dimensionality reduction by locally linear embedding. Neural Inf. Processing Systems, 2002.
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    • Polito, M.1    Perona, P.2
  • 9
    • 2342517502 scopus 로고    scopus 로고
    • Think globally, fit locally: Unsupervised learning of low dimensional manifolds
    • S. Roweis and L. Saul. Think globally, fit locally: Unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research, 4:119-155, 2003.
    • (2003) Journal of Machine Learning Research , vol.4 , pp. 119-155
    • Roweis, S.1    Saul, L.2
  • 10
    • 33745824263 scopus 로고    scopus 로고
    • K. Schindler, J. U, and H. Wang. Perspective n -view multibody structure-and-motion through model selection. European Conference on Computer Vision, pages 606-619, 2006.
    • K. Schindler, J. U, and H. Wang. Perspective n -view multibody structure-and-motion through model selection. European Conference on Computer Vision, pages 606-619, 2006.
  • 13
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, 2000.
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    • Tenenbaum, J.B.1    de Silva, V.2    Langford, J.C.3
  • 14
    • 0033556788 scopus 로고    scopus 로고
    • Mixtures of probabilistic principal component analyzers
    • M. Tipping and C. Bishop. Mixtures of probabilistic principal component analyzers. Neural Computation, 11(2):443-482, 1999.
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    • Tipping, M.1    Bishop, C.2
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    • Shape and motion from image streams under orthography
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    • A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, degenerate and nondegenerate
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.