Math @ Duke

Publications [#264833] of Guillermo Sapiro
Papers Published
 Black, MJ; Sapiro, G, Edges as outliers: Anisotropic smoothing using local image statistics, edited by Nielsen, M; Johansen, P; Olsen, OF; Weickert, J,
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1682
(January, 1999),
pp. 259270, SPRINGER, ISBN 354066498X [html], [doi]
(last updated on 2019/07/19)
Abstract: © SpringerVerlag Berlin Heidelberg 1999. Edges are viewed as statistical outliers with respect to local image gradient magnitudes. Within local image regions we compute a robust statistical measure of the gradient variation and use this in an anisotropic diffusion framework to determine a spatially varying edge stopping" parameter σ. We show how to determine this parameter for two edgestopping functions described in the literature (PeronaMalik and the Tukey biweight). Smoothing of the image is related the local texture and in regions of low texture, small gradient values may be treated as edges whereas in regions of high texture, large gradient magni tudes are necessary before an edge is preserved. Intuitively these results have similarities with human perceptual phenomena such as masking and popout. Results are shown on a variety of standard images.


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