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Concurrent grammar inference machines for 2-D pattern recognition: a comparison with the level set approach

Parallel processing promises scalable and effective computing power which can handle the complex data structures of knowledge representation languages efficiently. Past and present sequential architectures, despite the rapid advances in computing technology, have yet to provide such processing power and to offer a holistic solution to the problem. This paper presents a fresh attempt in formulating alternative techniques for grammar learning, based upon the parallel and distributed model of connectionism, to facilitate the more cognitively demanding task of pattern understanding. The proposed method has been compared with the contemporary approach of shape modelling based on level sets, and demonstrated its potential as a prototype for constructing robust networks on high performance parallel platforms.