JRM Vol.11 No.2 pp. 88-97
doi: 10.20965/jrm.1999.p0088


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

October 1, 1998
December 22, 1998
April 20, 1999
pattern detection, self-similarity analysis, stochastic-computational structure, capturing probability, symbolic observability

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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Last updated on Jan. 21, 2019