Schwartz O, Sejnowski TJ, Dayan P. (2006). Soft mixer assignment in a hierarchical generative model of natural scene statistics. Neural computation. 18 [PubMed]

See more from authors: Schwartz O · Sejnowski TJ · Dayan P

References and models cited by this paper

ATTNEAVE F. (1954). Some informational aspects of visual perception. Psychological review. 61 [PubMed]

Andrews D, Mallows C. (1974). Scale mixtures of normal distributions J Royal Stat Soc. 36

BARLOW HB. (1961). Possible principles underlying the transformations of sensory messages Sensory Communication.

Becker S. (1999). Implicit learning in 3D object recognition: the importance of temporal context. Neural computation. 11 [PubMed]

Bell AJ, Sejnowski TJ. (1997). The "independent components" of natural scenes are edge filters. Vision research. 37 [PubMed]

Bollerslev T, Engle RF, Nelson DB. (1994). ARCH models Handbook of Econometrics.

Brehm H, Stammler W. (1987). Description and generation of spherically invariant speech-model signals Signal Process. 12

Buccigrossi RW, Simoncelli EP. (1999). Image compression via joint statistical characterization in the wavelet domain. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 8 [PubMed]

Dempster AP, Laird NM, Rubin DB. (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B. 39

Einhäuser W, Kayser C, König P, Körding KP. (2002). Learning the invariance properties of complex cells from their responses to natural stimuli. The European journal of neuroscience. 15 [PubMed]

Field DJ. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America. A, Optics and image science. 4 [PubMed]

Field DJ. (1994). What is the goal of sensory coding? Neural Comput. 6

Foldiak P. (1991). Learning invariance from transformation sequences Neural Comput. 3

Fukushima K. (1980). Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics. 36 [PubMed]

Geisler WS, Perry JS, Super BJ, Gallogly DP. (2001). Edge co-occurrence in natural images predicts contour grouping performance. Vision research. 41 [PubMed]

Grenander U, Srivastava A. (2002). Probability models for clutter in natural images IEEE Trans Patt Anal And Mach Intel. 23

HUBEL DH, WIESEL TN. (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of physiology. 160 [PubMed]

Heeger DJ, Adelson EH, Simoncelli EP, Freeman WT. (1992). Shiftable multi-scale transforms IEEE Trans Information Theory. 38

Hinton GE, Ghahramani Z. (1997). Generative models for discovering sparse distributed representations. Philosophical transactions of the Royal Society of London. Series B, Biological sciences. 352 [PubMed]

Hinton GE, Ghahramani Z, Teh YW. (1999). Learning to parse images Advances in neural information processing systems. 11

Hoyer PO, Hyvärinen A. (2002). A multi-layer sparse coding network learns contour coding from natural images. Vision research. 42 [PubMed]

Hyvarinen A, Hoyer P. (2000). Emergence of topography and complex cell properties from natural images using extensions of ICA Advances in neural information processing systems. 12

Hyvärinen A, Hoyer P. (2000). Emergence of phase- and shift-invariant features by decomposition of natural images into independent feature subspaces. Neural computation. 12 [PubMed]

Hyvärinen A, Hurri J, Väyrynen J. (2003). Bubbles: a unifying framework for low-level statistical properties of natural image sequences. Journal of the Optical Society of America. A, Optics, image science, and vision. 20 [PubMed]

Jacobs RA, Barto AG, Jordan MI. (1991). Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks Cognitive Sci. 15

Jacobs RA, Hinton GE, Jordan MI, Nowlan SJ. (1991). Adaptive mixtures of local experts Neural Comput. 3

Karklin Y, Lewicki MS. (2003). Learning higher-order structures in natural images. Network (Bristol, England). 14 [PubMed]

Karklin Y, Lewicki MS. (2005). A hierarchical Bayesian model for learning nonlinear statistical regularities in nonstationary natural signals. Neural computation. 17 [PubMed]

Körding KP, Kayser C, Einhäuser W, König P. (2004). How are complex cell properties adapted to the statistics of natural stimuli? Journal of neurophysiology. 91 [PubMed]

Lee JS. (1980). Digital image enhancement and noise filtering by use of local statistics. IEEE transactions on pattern analysis and machine intelligence. 2 [PubMed]

Lee TW, Park HJ. (2005). Modeling nonlinear dependencies in natural images using mixture of Laplacian distribution Advances in neural information processing systems. 17

Lewicki MS, Karklin Y. (2003). A Model for learning variance components of natural images Advances in neural information processing systems. 15

Lewicki MS, Olshausen BA, Rao RPN. (2002). Probabilistic models of the brain: Perception and neural function.

Li Z. (2002). A saliency map in primary visual cortex. Trends in cognitive sciences. 6 [PubMed]

Li Z, Atick JJ. (1994). Toward a theory of the striate cortex. Neural Comput. 6

MacKay DJ. (2003). Information Theory, Inference and Learning Algorithms.

Mackay DM. (1956). The epistemological problem for automata Automata Studies.

Mato G, Parga N, Nadal JP, Turiel A. (1998). The self-similarity properties of natural images resemble those of turbulent flows Phys Rev Lett. 80

Mumford D, Huang J. (1999). Statistics of natural images and models Proc IEEE Conf Computer Vision and Pattern Recognition.

Neisser U. (1967). Cognitive Psychology.

Olshausen BA, Field DJ. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature. 381 [PubMed]

Olshausen BA, Sallee P. (2003). Learning sparse multiscale image representations Advances in neural information processing systems. 15

Osindero S, Welling M, Hinton GE. (2005). Topographic Product Models Applied to Natural Scene Statistics Neural Comput. 18

Parga N, Nadal JP. (1997). Redundancy reduction and independent component analysis: Conditions on cumulants and adaptive approaches Neural Comput. 9

Portilla J, Strela V, Wainwright MJ, Simoncelli EP. (2003). Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 12 [PubMed]

Rao RP, Ballard DH. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature neuroscience. 2 [PubMed]

Riesenhuber M, Poggio T. (1999). Hierarchical models of object recognition in cortex. Nature neuroscience. 2 [PubMed]

Romberg J, Choi H, Baraniuk R. (1999). Bayesian wavelet domain image modeling using hidden Markov trees Proc IEEE Intl Conf Image Proc.

Romberg JK, Choi H, Baraniuk RG. (2001). Bayesian tree-structured image modeling using wavelet-domain hidden Markov models. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 10 [PubMed]

Ruderman DL, Bialek W. (1994). Statistics of natural images: Scaling in the woods. Physical review letters. 73 [PubMed]

Rust NC, Schwartz O, Movshon JA, Simoncelli EP. (2005). Spatiotemporal elements of macaque v1 receptive fields. Neuron. 46 [PubMed]

Samaria F, Harter A. (1994). Parametrisation of a stochastic model for human face identification Paper presented at the Second IEEE Workshop on Applications of Computer Vision.

Schwartz O, Simoncelli EP. (2001). Natural signal statistics and sensory gain control. Nature neuroscience. 4 [PubMed]

Sejnowski TJ, Dayan P, Schwartz O. (2005). Assignment of multiplicativemixtures in natural images Advancesin Neural Information Processing Systems. 17

Shannon CE. (1948). The mathematical theory of communication Bell Syst Tech J. 27

Simoncelli E. (1997). Statistical models for images: Compression, restoration and synthesis Proceedings of the 31st Asilomar Conference on Signals, Systems and Computers.

Simoncelli E, Portilla J, Strela V. (2000). Image denoising using a local gaussian scale mixture model in the wavelet domain Proc SPIE 45th Annual Meeting.

Simoncelli E, Portilla J, Strela V, Wainwright M. (2001). Adaptive Wiener Denoising using a gaussian scale mixture model in the wavelet domain Proc 8th IEEE Intl Conf Image Proc.

Simoncelli EP, Olshausen BA. (2001). Natural image statistics and neural representation. Annual review of neuroscience. 24 [PubMed]

Simoncelli EP, Portilla J. (2003). Image restoration using gaussian scale mixtures in the wavelet domain Proc 10th IEEE Intl Conf Image Proc. 2

Simoncelli EP, Wainwright MJ. (2000). Scale mixtures of gaussians and the statistics of natural images Advances in neural information processing systems. 12

Simoncelli EP, Wainwright MJ, Willsky AS. (2001). Random cascades on wavelet trees and their use in modeling and analyzing natural imagery Appl Computational Harmonic Anal. 11

Srinivasan MV, Laughlin SB, Dubs A. (1982). Predictive coding: a fresh view of inhibition in the retina. Proceedings of the Royal Society of London. Series B, Biological sciences. 216 [PubMed]

Touryan J, Lau B, Dan Y. (2002). Isolation of relevant visual features from random stimuli for cortical complex cells. The Journal of neuroscience : the official journal of the Society for Neuroscience. 22 [PubMed]

Viola P, De_bonet J. (1997). A non-parametric multi-scale statistical model for natural images Advances in neural information processing systems. 9

Wallis G, Baddeley R. (1997). Optimal, unsupervised learning in invariant object recognition. Neural computation. 9 [PubMed]

Weaver W, Shannon CE. (1949). Nonlinear problems in random theory.

Williams CKI, Adams NJ. (1999). Dynamic trees Advances in neural information processing systems. 11

Williams CKI, Adams NJ. (2003). Dynamic trees for image modelling Image Vision Computing. 10

Wiskott L, Sejnowski TJ. (2002). Slow feature analysis: unsupervised learning of invariances. Neural computation. 14 [PubMed]

Zetzsche C, Barth E, Wegmann B. (1993). Nonlinear aspects of primary vision:Entropy reduction beyond decorrelation Intl Symposium Society for Information Display. 24

Zetzsche C, Nuding U. (2006). Nonlinear and higher-order approaches to the encoding of natural scenes. Network. 16

Zetzsche C, Wegmann B. (1990). Statistical dependence between orientation filter outputs used in an human vision based image code Proc SPIE Visual Comm Image Processing. 1360

van Hateren JH, van der Schaaf A. (1998). Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings. Biological sciences. 265 [PubMed]

References and models that cite this paper
This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.