Existence and Extension of Invariant Feature Associated with Self-Similar Patterns

Abstract

A computational scheme is presented for detecting invariant feature associated with self-similar patterns. The patterns to be detected are assumed to be generated through successive application of a set of contraction mappings. By identifying the mappings with generated attractors, a conditional probability is represented on a diffusion system that yields a discrete invariant set. This invariant set can be expanded to generate associative attractors. The detection scheme is verified through computer simulation.