Is early vision optimized for extracting higher-order dependencies?

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages 635-642

  Karklin, Yan ^a   Lewicki, Michael S ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BASIS FUNCTIONS; CONTEXT EFFECTS; EARLY VISION; FUNCTIONAL EXPLANATIONS; GAUSSIAN SCALE MIXTURES; HIERARCHICAL MODEL; IMAGE STRUCTURES; NATURAL IMAGES; NEURAL-PROCESSING; NONLINEAR BEHAVIOR; RECEPTIVE FIELDS; SIMPLE CELL; SPARSE CODING; SPATIAL SCALE; STATISTICAL MODELS;

DISCRETE COSINE TRANSFORMS; HIERARCHICAL SYSTEMS; IMAGE ANALYSIS; OPTIMIZATION;

INDEPENDENT COMPONENT ANALYSIS;

EID: 84864056418     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. A. J. Bell and T. J. Sejnowski. The 'independent components' of natural scenes are edge filters. Vision Research, 37(23):3327-3338, 1997.
2. B. A. Olshausen and D. J. Field. Emergence of simple-cell receptive-field properties by learning a sparse code for natural images. Nature, 381:607-609, 1996.
3. D. F. Andrews and C. L. Mallows. Scale mixtures of normal distributions. Journal of the Royal Statistical Society B, 36(1):99-102, 1974.
4. M. J. Wainwright, E. P. Simoncelli, and A. S. Willsky. Random cascades on wavelet trees and their use in analyzing and modeling natural images. Applied Computational and Harmonic Analysis, 11:89-123, 2001.
5. A. Hyvärinen, P. O. Hoyer, and M. Inki. Topographic independent component analysis. Neural Computation, 13:1527-1558, 2001.
6. Y. Karklin and M.S. Lewicki. A hierarchical bayesian model for learning non-linear statistical regularities in non-stationary natural signals. Neural Computation, 17:397-423, 2005.
7. O. Schwartz and E. P. Simoncelli. Natural signal statistics and sensory gain control. Nat. Neurosci., 4:819-825, 2001.
8. D. Field. What is the goal of sensory coding. Neural Computation, 6:559-601, 1994.
9. D. R. Ruderman and W. Bialek. Statistics of natural images: Scaling in the woods. Physical Review Letters, 73(6):814-818, 1994.
10. J. H. van Hateren and A. van der Schaaf. Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society, London B, 265:359-366, 1998.
11. J. P. Jones and L. A. Palmer. An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology, 58(6):1233-1258, 1987.
12. E. Doi and M. S. Lewicki. Sparse coding of natural images using an overcomplete set of limited capacity units. In Advances in Neural Processing Information Systems 18, 2004.
13. R. L. De Valois, D. G. Albrecht, and L. G. Thorell. Spatial frequency selectivity of cells in macaque visual cortex. Vision Research, 22:545-559, 1982.
14. D. L. Ringach. Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex. Journal of Neurophysiology, 88:455-463, 2002.
15. J. Portilla, V. Strela, M. J. Wainwright, and E.P. Simoncelli. Image denoising using Gaussian scale mixtures in the wavelet domain. IEEE Transactions on Image Processing, 12:1338-1351, 2003.
16. Y. Karklin and M.S. Lewicki. Learning higher-order structures in natural images. Network: Computation in Neural Systems, 14:483-499, 2003.
17. C. Zetzsche and G. Krieger. Nonlinear neurons and highorder statistics: New approaches to human vision and electronic image processing. In B. Rogowitz and T.V. Pappas, editors, Proc. SPIE on Human Vision and Electronic Imaging IV, volume 3644, pages 2-33, 1999.
18. M. S. Lewicki and B. A. Olshausen. A probabilistic framework for the adaptation and comparison of image codes. Journal of the Optical Society of America A, 16(7):1587-1601, 1999.
19. B. A. Olshausen and D. J. Field. Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37(23), 1997.