Title :
Maximum entropy contouring and clustering for fractal attractors with application to self-similarity coding of complex texture
Author_Institution :
Fac. of Eng., Osaka Inst. of Technol., Osaka, Japan
Abstract :
Based on stochastic modeling of self-similarity processes. capturing probability for not-yet-identified pattern is represented by multi-scale image. Through detecting level set and local maxima of the multi-scale image, smooth contours and finite feature pattern are estimated for fractal attractors. Estimated feature pattern is clustered within the framework of entropy maximization to design a system of reduced affine mappings with fixed points on boundary. Geometric-structural consistency of designed code is verified through computer simulation.
Keywords :
entropy codes; fractals; image coding; image representation; image texture; pattern clustering; probability; stochastic processes; stochastic programming; complex image texture; entropy maximization; finite feature pattern estimation; fractal attractor; geometric-structural consistency; maximum entropy contouring; multiscale image representation; pattern clustering; probability; reduced affine mapping; self-similarity image coding; stochastic modeling; Agricultural machinery; Complexity theory; Entropy; Feature extraction; Fractals; Image restoration; Imaging;
Conference_Titel :
Signal Processing Conference (EUSIPCO 1998), 9th European
Conference_Location :
Rhodes
Print_ISBN :
978-960-7620-06-4
