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Proceedings Paper

Neural Networks for Computer Vision: A Framework for Specifications of a General Purpose Vision System
Author(s): Josef Skrzypek; Edmond Mesrobian; David Gungner
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Paper Abstract

The development of autonomous land vehicles (ALV) capable of operating in an unconstrained environment has proven to be a formidable research effort. The unpredictability of events in such an environment calls for the design of a robust perceptual system, an impossible task requiring the programming of a system bases on the expectation of future, unconstrained events. Hence, the need for a "general purpose" machine vision system that is capable of perceiving and understanding images in an unconstrained environment in real-time. The research undertaken at the UCLA Machine Perception Laboratory addresses this need by focusing on two specific issues: 1) the long term goals for machine vision research as a joint effort between the neurosciences and computer science; and 2) a framework for evaluating progress in machine vision. In the past, vision research has been carried out independently within different fields including neurosciences, psychology, computer science, and electrical engineering. Our interdisciplinary approach to vision research is based on the rigorous combination of computational neuroscience, as derived from neurophysiology and neuropsychology, with computer science and electrical engineering. The primary motivation behind our approach is that the human visual system is the only existing example of a "general purpose" vision system and using a neurally based computing substrate, it can complete all necessary visual tasks in real-time.

Paper Details

Date Published: 29 March 1989
PDF: 14 pages
Proc. SPIE 1076, Image Understanding and the Man-Machine Interface II, (29 March 1989); doi: 10.1117/12.952674
Show Author Affiliations
Josef Skrzypek, University of California (United States)
Edmond Mesrobian, University of California (United States)
David Gungner, University of California (United States)


Published in SPIE Proceedings Vol. 1076:
Image Understanding and the Man-Machine Interface II
Eamon B. Barrett; James J. Pearson, Editor(s)

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