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<p>The general principles of detection, classification, and measurement of discontinuities are studied. The following issues are discussed: detecting the location of discontinuities; classifying discontinuities by their degrees; measuring the size of discontinuities; and coping with the random noise and designing optimal discontinuity detectors. An algorithm is proposed for discontinuity detection from an input signal S. For degree k discontinuity detection and measurement, a detector (P, Phi ) is used, where P is the pattern and Phi is the corresponding filter. If there is a degree k discontinuity at location t/sub 0/, then in the filter response there is a scaled pattern alpha P at t/sub 0/, where alpha is the size of the discontinuity. This reduces the problem to searching for the scaled pattern in the filter response. A statistical method is proposed for the approximate pattern matching. To cope with the random noise, a study is made of optimal detectors, which minimize the effects of noise.</p>
discontinuities; computer vision; detection; classification; random noise; optimal discontinuity detectors; scaled pattern; statistical method; approximate pattern matching; computer vision; statistics

D. Lee, "Coping with Discontinuities in Computer Vision: Their Detection, Classification, and Measurement," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 12, no. , pp. 321-344, 1990.
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