Regularization of inverse visual problems involving discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-Dimensional Regularization with Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection, classification, and measurement of discontinuities
SIAM Journal on Scientific and Statistical Computing
From Images to Surfaces: A Computational Study of the Human Early Visual System
From Images to Surfaces: A Computational Study of the Human Early Visual System
Digital Image Processing
A Regularized Solution to Edge Detection
A Regularized Solution to Edge Detection
Volterra Integral and Differential Equations: SECOND EDITION (Mathematics in Science and Engineering)
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Edges at Subpixel Accuracy Using the Local Energy Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Sciences: an International Journal
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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.