NIPS Workshop: Object Features for Visual Shape Representation

[Nathan Intrator]

First announcement of the following workshop.  Updates and call for
presentations is on the web page.

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	  Object Features for Visual Shape Representation
        
            NIPS-95 Workshop: Saturday, Dec 2, 1995

          Organizers:  Shimon Edelman,  Nathan Intrator

        http://www.physics.brown.edu/~nin/workshop95.html

Overview

Object recognition can be regarded as a comparison between the
stimulus shape and a library of reference shapes stored in long-term
memory.  It is not likely that the visual system stores exact
templates or snapshots of familiar objects, both for pragmatic reasons
(the appearance of a 3D object depends on the viewing conditions,
making a close match between the stimulus and a template
unattainable), and because of computational limitations that have to
do with the curse of dimensionality. Many competing approaches to the
extraction of features useful for shape representation have been
proposed in the past. 

The workshop will explore and compare some of these approaches.  We
shall be particularly interested in discussing different approaches
for evaluating feature extraction rules: information theory,
statistics, pattern recognition etc.  We would like to elaborate on
the goal of feature/information extraction in early visual cortex, the
relevance of the statistics of the input environment to studying
learning rules and comparison between visual cortical plasticity
models. <I> Presentation of psychophysical and neurobiological data
relevant to the feature issue will be encouraged.</I>

POTENTIAL PARTICIPANTS:

connectionists / feature extraction people / vision researchers /
neurobiologists working on perceptual learning

Invited Speakers
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Joseph Atick, Rockefeller (Tentative) 
Horace Barlow, Cambridge (Tentative) 
Elie Binenstock, CNRS, Brown 
Ichiro Fujita, Osaka
Stu Geman, Brown
Tai Sing Lee, Harvard
Bruno A. Olshausen, Cornell
Tommy Poggio, MIT
Dan Ruderman, USC (Tentative) 
Harel Shouval, Brown