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) ---------------------------------------------------------------------------- -------- 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