Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/99464
Title: Visual schemas in neural networks for object recognition and scene analysis
Authors: Leow, W.K. 
Miikkulainen, R.
Keywords: Circular reaction
Cooperation and competition
Object recognition
Perceptual reversal
Priming
Robustness
Scene analysis
Schema hierarchy
Schema learning
Stability
Structured neural networks
Visual schemas
Issue Date: 1997
Source: Leow, W.K.,Miikkulainen, R. (1997). Visual schemas in neural networks for object recognition and scene analysis. Connection Science 9 (2) : 161-200. ScholarBank@NUS Repository.
Abstract: VISOR is a large connectionist system that shows how visual schemas can be learned, represented and used through mechanisms natural to neural networks. Processing in VISOR is based on cooperation, competition, and parallel bottom-up and top-down activation of schema representations. VISOR is robust against noise and variations in the inputs and parameters. It can indicate the confidence of its analysis, pay attention to important minor differences, and use context to recognize ambiguous objects. Experiments also suggest that the representation and learning are stable, and behavior is consistent with human processes such as priming, perceptual reversal and circular reaction in learning. The schema mechanisms of VISOR can serve as a starting point for building robust high-level vision systems, and perhaps for schema-based motor control and natural language processing systems as well. © 1997 Carfax Publishing Ltd.
Source Title: Connection Science
URI: http://scholarbank.nus.edu.sg/handle/10635/99464
ISSN: 09540091
Appears in Collections:Staff Publications

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