Title :
Multi-scale image analysis for stochastic detection of self-similarity in complex texture
Author_Institution :
Fac. of Eng., Osaka Inst. of Technol., Japan
Abstract :
A method is presented for detecting self-similarity via multi-scale image analysis. By integrating multi-scale images, the missing probability is represented for unknown attractors. This implies that the collection of local minimum points of the missing probability specifies the stochastic feature of observed patterns. The self-similarity is detected via the design of the imaging process generating the most complex pattern. The method is verified by simulation studies
Keywords :
fractals; image texture; pattern recognition; probability; stochastic processes; complex pattern; complex texture; fractals; local minimum points; multi-scale image analysis; pattern detection; probability; self-similarity stochastic detection; simulation; unknown attractors; Fractals; Image converters; Image motion analysis; Image resolution; Image sequence analysis; Image texture analysis; Layout; Process design; Stochastic processes; Visual perception;
Conference_Titel :
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-4053-1
DOI :
10.1109/ICSMC.1997.637356
