[vslist] JOSA special issue on Bayesian and statistical approaches to vision

[David Knill]

	JOSA A is planning a special issue on Bayesian and 
statistical approaches to vision to be published in summer, 2003. I 
have enclosed the announcement below and would like to encourage 
interested researchers to consider submitting a paper for the issue. 
The deadline for submission is Oct. 1, 2002. If you have any 
questions about submissions, you can contact one of the guest editors 
listed at the bottom of the announcement.

David Knill, Bill Geisler and Bill Freeman (guest editors)

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JOSA announcement:

		The editors of JOSA A are soliciting papers for a 
special issue on
                                Bayesian and statistical approaches to 
vision. The special issue will
                                span topics in human visual 
perception, computer vision and neural
                                coding of visual information.

                                The past decade has seen an explosion 
of interest in the application
                                of statistical techniques to modeling 
vision in both biological and
                                artificial systems. Vision is 
fundamentally a problem of making
                                inferences about the world from 
uncertain information. How sensory
                                noise and statistical regularities in 
the environment structure
                                problems of visual coding is central 
to a computational understanding
                                of vision. The mathematics of 
statistics, stochastic processes and
                                Bayesian inference provide the natural 
framework for understanding
                                these aspects of visual processing. 
They have also provided a fertile
                                framework for linking computational 
theories of vision problems to
                                models of information processing in 
biological systems.

                                In the domain of computer vision, 
recent advances in algorithms for
                                performing optimal statistical 
inference have opened the door to a
                                broader array of implementations of 
working systems built on
                                rigorous statistical characterizations 
of visual problems. These have
                                led to more robust and effective 
algorithms for problems ranging
                                from 3D estimation to object 
recognition. In biological vision,
                                statistical signal processing has 
provided the framework for rigorous
                                models relating neural coding to the 
statistical structure of natural
                                images. Researchers have extended the 
application of ideal observer
                                models to higher level visual problems 
such as perceptual
                                organization, depth perception and 
object recognition. Bayesian
                                models of perceptual performance have 
also begun to emerge to
                                explain a wide assortment of 
psychophysical phenomena in higher
                                level vision.

                                Suggested topics for submission 
include, but are not limited to

                                       Natural image statistics and 
computer vision
                                       Natural image statistics and 
neural coding
                                       Bayesian approaches in computer vision
                                       Applications of ideal observer models
                                       Statistical / Bayesian models 
of perceptual performance
                                       Applications of decision theory 
to perception
                                       Signal detection models of 
recognition and attention
                                       Statistical learning in machine 
and biological vision

                                                         Feature Editors
                                  David Knill
                                  University of Rochester
                                  Rochester, New York
                                  knill@cvs.rochester.edu

                                  William T. Freeman
                                  Massachusetts Inst. Of Technology
                                  Cambridge, Massachusetts
                                  wtf@mit.edu

                                  Wilson S. Geisler
                                  University of Texas
                                  Austin, Texas
                                  geisler@phy.utexas.edu
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David C. Knill, Ph.D.
Dept. of Brain and Cognitive Science and
Center for Visual Science
University of Rochester
274 Meliora Hall
Rochester, NY 14627
(716) 275-4597

http://www.cvs.rochester.edu/people/d_knill/home_page.html