Visual Processing of Natural Images:
Theory, Psychophysics, Physiology, & Imaging

Friday & Saturday: April 5 & 6, 2002
University of Minnesota
LOCATION: Basic Sciences & Biomedical Engineering (BSBE 2-101)

Sponsored by:
The NSF/IGERT Program in Computational Neuroscience*

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Within less than a decade of the birth of information theory in the late 1940's, a number of scientists proposed that the theory could provide a link between environmental statistics and neural processing through efficient 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 high-dimensional image data sets, model these data, and propose quantiative 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. This symposium brings together interdisciplinary research representing theoretical, neurophysiological, brain imaging, and psychophysical approaches to the problem of visual processing of natural images. Key goals will be the discussion of the bridge between simple and complex (natural) images, and extensions of the information theoretic perspective to understanding higher level visual processing.

Registration

Program Schedule

Posters

Psy 8031/8036

 

  

Speaker
Institution
Talk Title
Geoff Boynton The Salk Institute Using fMRI to compare cortical magnification factors in human V1 to visual acuity
Ron Dror MIT Illumination Statistics and Surface Reflectance Recognition
David Field Cornell Natural scenes and the depth of statistical knowledge
William Freeman MIT

Learning to estimate missing high-resolution details
Jack Gallant UC Berkeley The role of area V4 in natural vision
Bill Geisler UT Austin Natural Scene Statistics and the Evolution of Perceptual Systems
Sheng He U. Minnesota What adaptation can tell us about processing of natural images?
Aapo Hyvarinen Helsinki U. Technology Beyond independence and sparseness in models of natural image statistics
Dae-Shik Kim U. Minnesota What does neuroimaging tell us about visual coding strategies?
Daniel Kersten U. Minnesota From Image Statistics to Visual Inference
Don Macleod UCSD Color discrimination, color constancy, and natural scene statistics
Bruno Olshausen UC Davis Sparse coding as a principle of image representation in visual cortex
Gregor Rainer MPI Tübingen Non-monotonic noise tuning of the BOLD signal to natural images in monkey primary visual cortex
Pamela Reinagel Harvard Coding of natural visual stimuli in the thalamus: What exactly is optimized?
Dario Ringach UCLA Testing Theories of Natural Image Representation in Primary Visual Cortex
Eero Simoncelli NYU Neural gain control: ecological justification and characterization

Program Organizers: Daniel Kersten, Sheng He,Gordon Legge, Paul Schrater, Cheryl Olman, Sing-Hang Cheung, Tom Carlson, Thomas Naselaris, Patty Costello.

NSF/IGERT Program Adminstrator: Kathleen Clinton

*With additional support from the Center for Cognitive Sciences, the Graduate School, Institute of Technology, Medical School, and Supercomputing Institute of the University of Minnesota.