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<p>An edge detection and surface reconstruction algorithm in which the smoothness is controlled spatially over the image space is presented. The values of parameters in the model are adaptively determined by an iterative refinement process; hence, the image-dependent parameters such as the optimum value of the regularization parameter or the filter size are eliminated. The algorithm starts with an oversmoothed regularized solution and iteratively refines the surface around discontinuities by using the structure exhibited in the error signal. The spatial control of smoothness is shown to resolve the conflict between detection and localization criteria of edge detection by smoothing the noise in continuous regions while preserving discontinuities. The performance of the algorithm is quantitatively and qualitatively evaluated on real and synthetic images, and it is compared with those of Marr-Hildreth and Canny edge detectors.</p>
filtering; surface reconstruction; refined regularization; edge detection; image space; iterative refinement process; smoothness; localization criteria; edge detection; filtering and prediction theory; image reconstruction; iterative methods

C. Li and M. Gökmen, "Edge Detection and Surface Reconstruction Using Refined Regularization," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 492-499, 1993.
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