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Volumn , Issue , 2005, Pages

Modeling nonlinear dependencies in natural images using mixture of laplacian distribution

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; HIERARCHICAL SYSTEMS; IMAGE SEGMENTATION; MIXTURES;

EID: 33645816664     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (20)

References (10)
  • 1
    • 0030832881 scopus 로고    scopus 로고
    • The 'independent components' of natural scenes are edge filters
    • A. J. Bell and T. J. Sejnowski, The 'Independent Components' of Natural Scenes are Edge Filters, Vision Research, 37(23):3327-3338, 1997.
    • (1997) Vision Research , vol.37 , Issue.23 , pp. 3327-3338
    • Bell, A.J.1    Sejnowski, T.J.2
  • 2
    • 0348223982 scopus 로고    scopus 로고
    • Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood, estimation
    • A. Hyvarinen, Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation, Neural Computation, 11(7):1739-1768, 1999.
    • (1999) Neural Computation , vol.11 , Issue.7 , pp. 1739-1768
    • Hyvarinen, A.1
  • 3
    • 0034290916 scopus 로고    scopus 로고
    • Ica mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind separation
    • October
    • T. Lee, M. Lewicki, and T. Sejnowski., ICA Mixture Models for unsupervised Classification of non-gaussian classes and automatic context switching in blind separation. PAMI, 22(10), October 2000.
    • (2000) PAMI , vol.22 , pp. 10
    • Lee, T.1    Lewicki, M.2    Sejnowski, T.3
  • 5
    • 84898985290 scopus 로고    scopus 로고
    • Emergence of phase and shift invariant features by decomposition of natural images into independent feature subspaces
    • A. Hyvarinen, P. O. Hoyer. Emergence of phase and shift invariant features by decomposition of natural images into independent feature subspaces. Neurocomputing, 1999.
    • (1999) Neurocomputing
    • Hyvarinen, A.1    Hoyer, P.O.2
  • 6
    • 17144449542 scopus 로고    scopus 로고
    • Topographic independent component analysis as a model of v1 receptive fields
    • June
    • A. Hyvarinen, P.O. Hoyer, Topographic Independent component analysis as a model of V1 Receptive Fields, Neurocomputing, Vol. 38-40, June 2001.
    • (2001) Neurocomputing , vol.38-40
    • Hyvarinen, A.1    Hoyer, P.O.2
  • 7
    • 79953038910 scopus 로고    scopus 로고
    • Learning sparse topographic representations with products of student-t distributions
    • M. Welling and G. E. Hinton, S. Osindero, Learning Sparse Topographic Representations with Products of Student-t Distributions, NIPS, 2002.
    • (2002) NIPS
    • Welling, M.1    Hinton, G.E.2    Osindero, S.3
  • 8
    • 0347517574 scopus 로고    scopus 로고
    • Learning higher-order structures in natural images
    • August
    • M. S. Lewicki and Y. Karklin, Learning higher-order structures in natural images, Network: Comput. Neural Syst. 14 (August 2003) 483-499.
    • (2003) Network: Comput. Neural Syst. , vol.14 , pp. 483-499
    • Lewicki, M.S.1    Karklin, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.