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Proceedings Paper

Biomimetic sensory abstraction using hierarchical quilted self-organizing maps
Author(s): Jeffrey W. Miller; Peter H. Lommel
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Paper Abstract

We present an approach for abstracting invariant classifications of spatiotemporal patterns presented in a high-dimensionality input stream, and apply an early proof-of-concept to shift and scale invariant shape recognition. A model called Hierarchical Quilted Self-Organizing Map (HQSOM) is developed, using recurrent self-organizing maps (RSOM) arranged in a pyramidal hierarchy, attempting to mimic the parallel/hierarchical pattern of isocortical processing in the brain. The results of experiments are presented in which the algorithm learns to classify multiple shapes, invariant to shift and scale transformations, in a very small (7×7 pixel) field of view.

Paper Details

Date Published: 2 October 2006
PDF: 10 pages
Proc. SPIE 6384, Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision, 63840A (2 October 2006); doi: 10.1117/12.686183
Show Author Affiliations
Jeffrey W. Miller, Draper Lab. (United States)
Peter H. Lommel, Draper Lab. (United States)


Published in SPIE Proceedings Vol. 6384:
Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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