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|Title:||Cellular automata in pattern recognition||Authors:||Raghavan, R.||Issue Date:||May-1993||Citation:||Raghavan, R. (1993-05). Cellular automata in pattern recognition. Information Sciences 70 (1-2) : 145-177. ScholarBank@NUS Repository.||Abstract:||This paper is a review of recent published work in the application of automata networks as part of a pattern or image recognition system. The principal requirements were to integrate model-based and data-driven approaches within a connectionist framework and to allow full parallelism. In particular, we construct a network of probabilistic cellular automata (PCAs) for iteratively resolving ambiguities and conflicts in pattern recognition. A natural implementation of inductive inference rules in such a network results in a dynamics that is sensitive to nonsymmetric couplings (synaptic weights), unlike that of the more common models inspired by statistical physics (e.g., the Boltzmann machine). This, along with full parallelism, means that object recognition must be achieved through the intermediate-time rather than "infinite" -time behavior of the system. Another, more technology-driven, feature includes using local inferences insofar as possible. The framework is translation-invariant, which is natural for image recognition. This leads to a different architecture for describing the model from that used in Bayesian inference networks. © 1993.||Source Title:||Information Sciences||URI:||http://scholarbank.nus.edu.sg/handle/10635/111149||ISSN:||00200255|
|Appears in Collections:||Staff Publications|
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