Existence and Detection of Invariant Feature Associated with Conditional Distribution Induced by Self-Similar Patterns

Abstract

A method is presented for detecting self-similar patterns. A version of conditional distribution is introduced to self-similar patterns for representing computable morphological feature. The morphological feature associated with accurately observed attractor is proved to yield a subset that is invariant with respect to self-similarity mapping. The invariant feature is demonstrated to activate an imaging process for generating proximately located attractor. An algorithm was developed for computing invariant feature and verified through simulation study.