Visual Processing of Natural Images

(Psy 8031, 8036, 8993)*

University of Minnesota, Spring Semester, 2002
http://courses.kersten.org

Instructors: Daniel Kersten and Sheng He
Contact: kersten@umn.edu, 612-625-2589 , URL: www.umn.edu/~kersten


Meeting time: 12:00 to 1:30 Thursdays, beginning February 7th
Meeting Place: Elliott Hall N19 (Basement). For access, contact kersten@umn.edu

Within less than a decade of the birth of information theory in the 1940's, a number of scientists proposed that the theory could provide a link between environmental statistics and neural processing through efficient information coding. In particular, it was conjectured that the brain's processing of retinal images could be interpreted in terms of codes that maximized information transfer about the world despite sensory noise, and that it did this by exploiting the statistics of natural images. Apart from a handful of subsequent studies in the following decades, it wasn't until the 1990's that theoretical developments in statistical modeling and computation were brought together to analyze large sets of image data, model these data, and propose quantitative neural models of visual processing. Comparison with neural data has shown that several fundamental properties of the visual system at both retinal and cortical levels can be understood in terms of adaptation to the statistical properties of the images received. We will read and discuss key papers on this topic. A goal will be the discussion of extensions of this perspective to understanding higher level visual processing.

This seminar is connected with a Computational Neuroscience Symposium on Visual Processing of Natural Images to be held at the University of Minnesota April 5 and 6, 2002.

 

*If you only want 2 credits, register for: Psy 8031Seminar: Visual Perception: 52927

*If you want 3 credits and wish do to a final written project, register for: Sheng He's Psy 8993 Directed Studies: Special Areas of Psychology and Related Sciences : 61340

*If you want 3 credits and wish to do a final computational project, register for: Dan Kersten's Psy 8036 Special Topics in Computational Vision: 64652.


Reading List

A.

Attneave, F. (1954). Some Informational Aspects of Visual Perception. Psychological Review, 61((3)), 183-193.

Barlow, H. B. (1961). Possible principles underlying the transformation of sensory messages. In W. A. Rosenblith (Ed.), Sensory Communication. Cambridge, MA: MIT Press.

Barlow, H. B., & Foldiak, P. (1989). Adaptation and decorrelation in the cortex. In C. Miall & R. M. Durban & G. J. Mitchison (Eds.), The Computing Neuron: Addison-Wesley.

Field, D. J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A, 4(12), 2379-2394.

He, S., & MacLeod, D.I. (2001). Orientation-selective adaptation and tilt after-effect from invisible patterns. Nature, 411 (6836), 473-476.

He, S., Cohen, E.R., & Hu, X. (1998). Close correlation between activity in brain area MT/V5 and the perception of a visual motion aftereffect. Curr Biol, 8 (22), 1215-1218.

Kersten, D. (1987). Predictability and redundancy of natural images. J Opt Soc Am A, 4(12), 2395-2400.

Vinje, W. E. & Gallant, J. L. (2000). Sparse coding and decorrelation in primary visual cortex during natural vision. Science, 287, 1273-6.

Olshausen, B. A. & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607-609.

Simoncelli, E. P., & Olshausen, B. A. (2001). Natural image statistics and neural representation. Annu Rev Neurosci, 24, 1193-1216.

Schwartz, O., & Simoncelli, E. P. (2001). Natural signal statistics and sensory gain control. Nat Neurosci, 4(8), 819-825.

Wainwright, M. J. (1999). Visual adaptation as optimal information transmission. Vision Research, 39, 3960--3974.

B.

Bell, A. J. & Sejnowski, T. J. (1997). The "independent components" of natural scenes are edge filters. Vision Res, 37, 3327-38.

Gallant, J. L., Connor, C. E. & Van Essen, D. C. (1998). Neural activity in areas V1, V2 and V4 during free viewing of natural scenes compared to controlled viewing [corrected and republished article originally printed in Neuroreport 1998 Jan 5;9(1):85-90]. Neuroreport, 9, 2153-8.

Knill, D. C., Field, D. & Kersten, D. (1990). Human discrimination of fractal images. Journal of the Optical Society of America, A, 7, 1113-1123.

Laughlin, S. B. (1981). A simple coding procedure enhances a neuron's information capacity. Z. Naturforsch.

C.

Leopold DA, O'Toole AJ, Vetter T, Blanz V. (2001) Prototype-referenced shape encoding revealed by high-level aftereffects. Nat Neurosci 4(1):89-94

Nothdurft, H. C., Gallant, J. L. & Van Essen, D. C. (1999). Response modulation by texture surround in primate area V1: correlates of "popout" under anesthesia. Vis Neurosci, 16, 15-34.

*Regan, B.C., Julliot, C., Simmen, B., Vienot, F., Charles-Dominique, P., & Mollon, J.D. (2001). Fruits, foliage and the evolution of primate colour vision. Philos Trans R Soc Lond B Biol Sci, 356 (1407), 229-283.

*Webster, M.A., & Mollon, J.D. (1997). Adaptation and the color statistics of natural images. Vision Res, 37 (23), 3283-3298.

Adaptation

Barlow, H. (1990). Conditions for versatile learning, Helmholtz's unconscious inference, and the task of perception. Vision Research, 30, 1561-1572.

Barlow, H.B., Macleod, D.I., & van Meeteren, A. (1976). Adaptation to gratings: no compensatory advantages found. Vision Res, 16 (10), 1043-1045.

Blakemore, C. & Campbell, F. W. (1969). On the existence of neurones in the human visual system selectively sensitive to the orientation and size of retinal images. Journal of Physiology, 203, 237-260.

Webster, M. A. & MacLin, O. H. (1999). Figural aftereffects in the perception of faces. Psychonomic Bulletin & Review, 6, 647-653.

Theory

Barlow, H.B. (1972). Single units and sensation: a neuron doctrine for perceptual psychology? Perception, 1 (4), 371-394.

Barlow, H.B. (1981). The Ferrier Lecture, 1980. Critical limiting factors in the design of the eye and visual cortex. Proc R Soc Lond B Biol Sci, 212 (1186), 1-34.

Kaas, J.H. (1997). Topographic maps are fundamental to sensory processing. Brain Res Bull, 44 (2), 107-112.

Lamme, V.A., Super, H., & Spekreijse, H. (1998). Feedforward, horizontal, and feedback processing in the visual cortex. Curr Opin Neurobiol, 8 (4), 529-535.

Lee, T.S. (1995). A Bayesian framework for understanding texture segmentation in the primary visual cortex. Vision Research, 35 (18), 2643-2657.

Lee, T.S., Mumford, D., Romero, R., & Lamme, V.A. (1998). The role of the primary visual cortex in higher level vision. Vision Res, 38 (15-16), 2429-2454.

Lennie, P. (1998). Single units and visual cortical organization. Perception, 27 (8), 889-935.

Mumford, D. (1994). Neuronal architectures for pattern-theoretic problems. In C. Koch, & J. L. Davis (Ed.), Large-Scale Neuronal Theories of the Brain. (pp. 125-152). Cambridge, MA: MIT Press.

Plumbley, M.D. (1999). Do cortical maps adapt to optimize information density? Network, 10 (1), 41-58.

Rao, R.P., & Ballard, D.H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects [see comments]. Nat Neurosci, 2 (1), 79-87.

Russell, G.S. (1997). Nested reentrant and recurrent computation in early vision: a Bayesian neuromorphic model applied to hyperacuity. Biol Cybern, 76 (3), 195-206.