Stochastic-Computational Approach to Self-Similarity Detection in Random Image Fields

Kohji Kamejima

doi:10.20965/jrm.1999.p0088

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JRM Vol.11 No.2 pp. 88-97

doi: 10.20965/jrm.1999.p0088

(1999)

Paper:

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Stochastic-Computational Approach to Self-Similarity Detection in Random Image Fields

Kohji Kamejima

Faculty of Engineering, Osaka Institute of Technology, 5-16-1, Omiya, Asahi, Osaka 535-8585, Japan

Received:

October 1, 1998

Accepted:

December 22, 1998

Published:

April 20, 1999

Keywords:

pattern detection, self-similarity analysis, stochastic-computational structure, capturing probability, symbolic observability

Abstract

We present an integrated stochastic-computational scheme for detecting self-similarity in random image fields. By modeling imaging as a Brownian motion in a successively reduced domain, capture probability is induced on the image plane. Attractor distribution is simultaneously identified with fixed points corresponding to mapping sequences generated by imaging. The computational structure of local maxima of capture probability is extracted through invariance and observability analysis to match observed attractors with a preassigned mapping dictionary. Proposed scheme was implemented as digital algorithm and verified through simulation.

Cite this article as:

K. Kamejima, “Stochastic-Computational Approach to Self-Similarity Detection in Random Image Fields,” J. Robot. Mechatron., Vol.11 No.2, pp. 88-97, 1999.

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