Christoph Raschke http://www.nd.edu/~crasche/
Postdoc in the Department of Psychology
University of Notre Dame
Abstract:
Visual object recognition occurs so rapid (<200ms), that one can
assume that the initial object percept is a hypothesis. But what are
useful descriptors for an object hypothesis? We propose that 2D spaces
(regions) are an important component of object description. Such
regions often represent surfaces of object parts, or silhouette
features - the space between parts. We have developed a neuromorphic
front-end that encodes space from gray-scale images using
wave-propagating neuronal networks: A retina network signals contours
by lines of spikes; subsequent cortical-like networks perform Blum's
symmetric-axis transform, a region-encoding mechanism that transforms
space into vectors useful for high-level representation. We discuss
how the vectors maybe integrated to form object hypotheses.