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Volumn , Issue , 2009, Pages 1818-1825

Fast Mean shift by compact density representation

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; FLOW GRAPHS; IMAGE SEGMENTATION; OBJECT TRACKING; STEREO VISION; TEXTURES;

EID: 70450214843     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206716     Document Type: Conference Paper
Times cited : (50)

References (23)
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    • Andoni, A.1    Indyk, P.2
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    • A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift
    • D. Barash and D. Comaniciu. A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift. Image and Vision Computing, 22(1): 73-81, 2004.
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    • The estimation of the gradient of a density function, with applications in pattern recognition
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    • Fukunaga, K.1    Hostetler, L.2
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    • I. Grabec. Self-organization of neurons described by the maximum-entropy principle. Biological Cybernetics, 63(5): 403-409, 1990.
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    • Grabec, I.1
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    • Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm
    • K. Kim, K. Jung, and J. Kim. Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12): 1631-1639, 2003.
    • (2003) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.25 , Issue.12 , pp. 1631-1639
    • Kim, K.1    Jung, K.2    Kim., J.3
  • 16
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    • Optimal approximations by piecewise smooth functions and associated variational problems
    • D. Mumford and J. Shah. Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math, 42(5): 577-685, 1989.
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    • Mumford, D.1    Shah., J.2
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    • A topological approach to hierarchical segmentation using mean shift
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    • A neural network approach to statistical pattern classification bysemiparametric'estimation of probability density functions
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.