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Proceedings Paper

Hierarchical network model for the analysis of human spatio-temporal information processing
Author(s): Kerstin Schill; Volker Baier; Florian Roehrbein; Wilfried Brauer
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Paper Abstract

The perception of spatio-temporal pattern is a fundamental part of visual cognition. In order to understand more about the principles behind these biological processes, we are analyzing and modeling the presentation of spatio-temporal structures on different levels of abstraction. For the low- level processing of motion information we have argued for the existence of a spatio-temporal memory in early vision. The basic properties of this structure are reflected in a neural network model which is currently developed. Here we discuss major architectural features of this network which is base don Kohonens SOMs. In order to enable the representation, processing and prediction of spatio-temporal pattern on different levels of granularity and abstraction the SOMs are organized in a hierarchical manner. The model has the advantage of a 'self-teaching' learning algorithm and stored temporal information try local feedback in each computational layer. The constraints for the neural modeling and data set for training the neural network are obtained by psychophysical experiments where human subjects' abilities for dealing with spatio-temporal information is investigated.

Paper Details

Date Published: 8 June 2001
PDF: 7 pages
Proc. SPIE 4299, Human Vision and Electronic Imaging VI, (8 June 2001); doi: 10.1117/12.429535
Show Author Affiliations
Kerstin Schill, Ludwig-Maximilians-Univ. Muenchen (Germany)
Volker Baier, Technische Univ. Muenchen (Germany)
Florian Roehrbein, Ludwig-Maximilians-Univ. Muenchen (Germany)
Wilfried Brauer, Technische Univ. Muenchen (Germany)

Published in SPIE Proceedings Vol. 4299:
Human Vision and Electronic Imaging VI
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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