29 March 1989 Neural Networks for Computer Vision: A Framework for Specifications of a General Purpose Vision System
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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.
© (1989) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Josef Skrzypek, Josef Skrzypek, Edmond Mesrobian, Edmond Mesrobian, David Gungner, David Gungner, } "Neural Networks for Computer Vision: A Framework for Specifications of a General Purpose Vision System", Proc. SPIE 1076, Image Understanding and the Man-Machine Interface II, (29 March 1989); doi: 10.1117/12.952674; https://doi.org/10.1117/12.952674
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