Self-similarity clustering of random texture via stochastic-computational complexity analysis

Author

Kamejima, Kohji

Author_Institution

Fac. of Eng., Osaka Inst. of Technol., Japan

fYear

1998

fDate

29-31 Jul 1998

Firstpage

1013

Lastpage

1018

Abstract

A nondeterministic scheme is presented for self-similar clustering of random texture. By modeling observed texture as the attractor associated with unknown contraction mappings, a capturing probability is induced on the image plane. Guided by maximum entropy growth of discrete stochastic features, the statistics of the mapping range is evaluated. Variance analysis is applied to estimate mapping parameters for partitioning the texture pattern into subregions of a fractal attractor. The proposed scheme was implemented and verified through simulation studies

Keywords

computational complexity; image texture; maximum entropy methods; parameter estimation; pattern clustering; probability; random processes; statistical analysis; capturing probability; discrete stochastic features; fractal attractor; maximum entropy growth; nondeterministic scheme; partitioning; random texture; self-similarity clustering; stochastic-computational complexity analysis; unknown contraction mappings; Entropy; Fractals; Image converters; Image segmentation; Image texture analysis; Layout; Pattern analysis; Probability; Statistics; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE '98. Proceedings of the 37th SICE Annual Conference. International Session Papers

Conference_Location

Chiba

Type

conf

DOI

10.1109/SICE.1998.742969

Filename

742969