Parallel Distributed Detection of an Invariant Feature Associated with Self-Similar Patterns

摘要

A parallel distributed scheme is presented for extracting a computable feature associated with self similar patterns. Observed patterns are assumed to be specified in terms of a set of contraction mappings that evokes an "avalanche of exploration" in image field. This intrinsically non-deterministic imaging process yields a conditional probability that is represented on a diffusion system. For identifying mapping set, a parallel projection algorithm is designed on a computable set of local minimums of the conditional distribution. The scheme is applied to dynamic detection of fractal patterns.