Neural network architecture for modeling the joint visual perception of orientation, motion, and depth

Authors:
Daniel Oberhoff;Andy Stynen;Marina Kolesnik
Affiliations:
Fraunhofer Institute for Applied Information Technology Schloss Birlinghoven, Sankt Augustin, Germany;Fraunhofer Institute for Applied Information Technology Schloss Birlinghoven, Sankt Augustin, Germany;Fraunhofer Institute for Applied Information Technology Schloss Birlinghoven, Sankt Augustin, Germany
Venue:
PIT'06 Proceedings of the 2006 international tutorial and research conference on Perception and Interactive Technologies
Year:
2006

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Abstract

We present a methodology and a neural network architecture for the modeling of low- and mid-level visual processing. The network architecture uses local filter operators as basic processing units which can be combined into a network via flexible connections. Using this methodology we design a neuronal network that models the joint processing of oriented contrast changes, their motion and depth. The network reflects the structure and the functionality of visual pathways. We present network responses to a stereo video sequence, highlight the correspondence to biological counterparts, outline the limitations of the methodology, and discuss specific aspects of the processing and the extent of visual tasks that can be successfully carried out by the suggested neuronal architecture.