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Unsupervised Optimization of Nonlinear Image Processing Filters Using Morphological Opening/Closing Spectrum and Genetic Algorithm
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2000/02/25
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Intelligent Signal and Image Processing)
mathematical morphology, pattern spectrum, nonlinear filter, filter optimization, learning, genetic algorithm,
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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.