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AN OPERATIONAL INFRASTRUCTURE FOR IMAGE PROCESSING
BASIC IMAGE PROCESSING OPERATIONS
STRUCTURAL OPERATIONS ON IMAGES
5 other sections not shown
algorithm application approach appropriate archetype architecture array Assign assume automated basic basis binarized image cell chapter character characteristics chosen class Q classification clustering component computational architectures computer vision configuration consider corresponding covariance matrix decision function decision rule DECISION THEORY defined described descriptors determine discussion distribution edge detection effect elements encoding Euclidean distance feature space gray level histogram gray scale Hough transform idea identify IEEE illustrated in Figure image data image processing implementation important interest linear measure n-tuple neighbors object area obtained original image parameters particular Patt pattern classes pattern recognition system pixel values possible practical situations problem procedure processor raw image representation represented robotic systems samples segments shown in Figure SIMD SISD spatial specific statistical statistically independent straight line structure task techniques tesselation test pattern thresholding tion training pattern transformed underlying vector vision systems visual XX XX XX XXX XXX XXX xxxx