Autonomous clustering of fractal patterns via Hausdorff potentials

Author

Kamejima, Kohji

Author_Institution

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

fYear

1999

fDate

36373

Firstpage

1077

Lastpage

1082

Abstract

A nondeterministic kinetics is introduced in image plane for autonomous clustering of fractal attractors associated with contraction mappings. By reformulating 2D Newton potential in terms of the Hausdorff distance, both the attribution to fixed points of contraction mappings and the consistency of fixed point estimates are evaluated. Attracted by fixed point estimates, feature points are aggregated to successively organize discrete clusters structurally consistent with the mapping set. The proposed scheme was implemented and verified through simulation studies

Keywords

feature extraction; fractals; image matching; stochastic processes; 2D Newton potential; Hausdorff potentials; autonomous clustering; contraction mappings; fixed point estimates; fractal attractors; fractal patterns; nearest neighbour aggregation; pattern clustering; self similarity; Data mining; Electrostatics; Fractals; Gravity; Image segmentation; Kinetic theory; Large-scale systems; Pattern clustering; Stochastic processes; Visual perception;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE Annual, 1999. 38th Annual Conference Proceedings of the

Conference_Location

Morioka

Print_ISBN

4-907764-13-8

Type

conf

DOI

10.1109/SICE.1999.788701

Filename

788701

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3105232