Unsupervised Optimization of Nonlinear Image Processing Filters Using Morphological Opening / Closing Spectrum and Genetic Algorithm(Special Section on Intelligent Signal and Image Processing)

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概要

• 論文の詳細を見る

• It is proposed a novel method that optimizes nonlinear filters by unsupervised learning using a novel definition of morphological pattern spectrum, called "morphological opening / closing spectrum(MOCS)."The MOCS can separate smaller portions of image objects from approximate shapes even if the shapes are degraded by noisy pixels.Our optimization method analogizes the linear low-pass filtering and Fourier spectrum:filter parameters are adjusted to reduce the portions of smaller sizes in MOCS, since they are regarded as the contributions of noises like high-frequency components.This method has an advantage that it uses only target noisy images and requires no example of ideal outputs.Experimental results of applications of this method to optimization of morphological open-closing filter for binary images are presented.

• 社団法人電子情報通信学会の論文
• 2000-02-25

著者

• Asano A

  Hiroshima Univ. Higashi Hiroshima Jpn

• ASANO Akira

  the Division of Mathematical and Information Sciences, Faculty of Integrated Arts and Sciences, Hiro

• Asano Akira

  The Division Of Mathematical And Information Sciences Faculty Of Integrated Arts And Sciences Hirosh

• Asano Akira

  The Department Of Mechanical System Engineering Kyushu Institute Of Technology

関連論文

• Unsupervised Optimization of Nonlinear Image Processing Filters Using Morphological Opening / Closing Spectrum and Genetic Algorithm(Special Section on Intelligent Signal and Image Processing)
• Morphological Multiresolution Pattern Spectrum
• Multiprimitive Texture Analysis Using Cluster Analysis and Morphological Size Distribution

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