Neural network modeling of visual recognition
Rafik Braham · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
The recognition of visual patterns is one of the main application areas of neural networks. Several models have been designed based on the current understanding of visual information processing in the brains of cats and monkeys. Examples of such models are described in the works of Fukushima, Grossberg, von der Malsburg, and others. But because the visual system is very complex and visual information processing consists of several stages, the technical models attempt to reproduce one or a few aspects. The author has been mostly interested in modeling some of the anatomical features of visual areas and understanding their functional significance. In this paper, principles used in popular models are analyzed. Then the structure and design rationale of a vision model is described. In this description, the principles of the model rather than its implementation details are underscored.