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Proceedings Paper

Review On Applications Of Neural Network To Computer Vision
Author(s): Wei Li; Nasser M. Nasrabadi
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Paper Abstract

Neural network models have many potential applications to computer vision due to their parallel structures, learnability, implicit representation of domain knowledge, fault tolerance, and ability of handling statistical data. This paper demonstrates the basic principles, typical models and their applications in this field. Variety of neural models, such as associative memory, multilayer back-propagation perceptron, self-stabilized adaptive resonance network, hierarchical structured neocognitron, high order correlator, network with gating control and other models, can be applied to visual signal recognition, reinforcement, recall, stereo vision, motion, object tracking and other vision processes. Most of the algorithms have been simulated on com-puters. Some have been implemented with special hardware. Some systems use features, such as edges and profiles, of images as the data form for input. Other systems use raw data as input signals to the networks. We will present some novel ideas contained in these approaches and provide a comparison of these methods. Some unsolved problems are mentioned, such as extracting the intrinsic properties of the input information, integrating those low level functions to a high-level cognitive system, achieving invariances and other problems. Perspectives of applications of some human vision models and neural network models are analyzed.

Paper Details

Date Published: 21 March 1989
PDF: 10 pages
Proc. SPIE 1004, Automated Inspection and High-Speed Vision Architectures II, (21 March 1989); doi: 10.1117/12.948979
Show Author Affiliations
Wei Li, Worcester Polytechnic Institute (United States)
Nasser M. Nasrabadi, Worcester Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 1004:
Automated Inspection and High-Speed Vision Architectures II
Michael J. W. Chen, Editor(s)

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