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Volumn 2006, Issue , 2006, Pages

Moving object segmentation using scene understanding

Author keywords

[No Author keywords available]

Indexed keywords

FRAME-TO-FRAME HOMOGRAPHY COMPUTATION; MOTION SEGMENTATION ALGORITHM; MOVING OBJECT; ZOOMING SENSOR;

EID: 33845540256     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2006.132     Document Type: Conference Paper
Times cited : (14)

References (16)
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  • 5
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    • A unified framework for tracking through occlusions and across sensor gaps
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    • (2005) Proc. CVPR , pp. 990-997
    • Kaucic, R.1    Perera, A.G.A.2    Brooksby, G.3    Kaufhold, J.4    Hoogs, A.5
  • 9
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    • Motion segmentation with accurate boundaries - A tensor voting approach
    • M. Nicolescu and G. Medioni. Motion segmentation with accurate boundaries - a tensor voting approach. In Proc. CVPR, 2003. 2, 6
    • (2003) Proc. CVPR
    • Nicolescu, M.1    Medioni, G.2
  • 10
    • 0033281701 scopus 로고    scopus 로고
    • Improved boosting algorithms using confidence-rated predictions
    • R. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297-336, 1999. 3
    • (1999) Machine Learning , vol.37 , Issue.3 , pp. 297-336
    • Schapire, R.1    Singer, Y.2
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    • A common framework for curve evolution, segmentation and anisotropic diffusion
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    • (1996) Proc. CVPR
    • Shah, J.1
  • 12
    • 0028112849 scopus 로고
    • Good features to track
    • J. Shi and C. Tomasi. Good features to track. In Proc. CVPR, pages 593-600, 1994. 2
    • (1994) Proc. CVPR , pp. 593-600
    • Shi, J.1    Tomasi, C.2
  • 13
    • 0032634283 scopus 로고    scopus 로고
    • Adaptive background mixture models for real-time tracking
    • C. Stauffer and E. Grimson. Adaptive background mixture models for real-time tracking. In Proc. CVPR, pages 246-252, 1999. 1
    • (1999) Proc. CVPR , pp. 246-252
    • Stauffer, C.1    Grimson, E.2
  • 14
    • 0033894631 scopus 로고    scopus 로고
    • MLESAC: A new robust estimator with application to estimating image geometry
    • P. Torr and A. Zisserman. MLESAC: A new robust estimator with application to estimating image geometry. CVIU, 78:138-156, 2000. 2
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  • 15
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    • Motion segmentation with missing data using powerfactorization and GPCA
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    • Statistical background modeling for non-stationary camera
    • C.-S. C. Ying Ren and Y. K. Ho. Statistical background modeling for non-stationary camera. Pattern Recognition Letters, 24, 2003. 1
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.