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Proceedings Paper

A Neural Network Architecture For Form And Motion Perception
Author(s): Stephen Grossberg
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Paper Abstract

This work further develops a neural network model of motion segmentation by visual cortex that was outlined in Grossberg (1987). We illustrate the model's properties by simulating on the computer data concerning group and element apparent notion, including the tendency for group notion to occur at longer ISIS and under conditions of short visual persistence. These phenomena challenge recent vision models because the switch between group and element motion is determined by changing temporal but not spatial display properties. The model clarifies the dependence of short-range and long-range motion on spatial scale. Its design specifies how oriented (x cell) and unoriented (y cell) detectors cooperate and compete in successive processing stages to generate notion signals that are sensitive to direction-of-notion, yet insensitive to direction-of-contrast. Tereus displays and Burt and Sperling displays generate appropriate motion signals in the circuit. Apparent motion and real motion generate testably different model properties. The model also clarifies how notion after-effects may be generated and how preprocessing of motion signals is joined to long-range cooperative notion mechanisms to control phenomena such as induced motion and notion capture. The total model systems is a motion Boundary Contour Systems (BCS) that is computed in parallel with the static BCS of Grossberg and Mingolla before both systems cooperate to generate a boundary representation for 3-D visual form perception.

Paper Details

Date Published: 1 March 1990
PDF: 1 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969763
Show Author Affiliations
Stephen Grossberg, Boston University (United States)


Published in SPIE Proceedings Vol. 1192:
Intelligent Robots and Computer Vision VIII: Algorithms and Techniques
David P. Casasent, Editor(s)

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