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MATHEMATICAL MORPHOLOGY I
OPERATORS LANGUAGES AND DECOMPOSITIONS
AUTOMATED OPERATOR DEVELOPMENT
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applied approach array binary image binary operation boundary closing components Computer Vision convergence convex hull convolution corresponding decomposition defined definition denoted detection dilation disk distance function edge detection equation erosion example extract Figure g-snake genetic algorithm given gray scale grey level histograms IEEE image algebra Image Analysis image processing implementation input integer lattice layer linear LOCUS LOCUSes mapping marker MasPar Mathematical Morphology Matheron matrix max-polynomial method morphological filters Morphological Image Morphological Image Processing morphological operations multiresolution Neumann polynomial neural noise objects obtained opening Operand optical optimization original image orthogonality output parameters peaks performed pixels point sets pointwise polynomial preprocessor problem regions reject set residue respectively result roof edges scale space segmentation shape signal soft spatial statistical structuring element subset target techniques template theorem threshold reduction transform translation variables vector voxels zero