Please use this identifier to cite or link to this item:
|Title:||Edge-Preserving Smoothing Using a Similarity Measure in Adaptive Geodesic Neighbourhoods|
|Authors:||GRAZZINI Jacopo; SOILLE Pierre|
|Citation:||PATTERN RECOGNITION vol. 42 no. 10 p. 2306-2316|
|Publisher:||PERGAMON-ELSEVIER SCIENCE LTD|
|Type:||Articles in periodicals and books|
|Abstract:||This paper introduces a novel image-dependent filtering approach derived from concepts known in mathematical morphology and aiming at edge-preserving smoothing natural images. Like other adaptive methods, it assumes that the neighbourhood of a pixel contains the essential information required for the estimation of local features. The proposed strategy does not require the definition of any spatial operator as it determines automatically, from the unfiltered input data, a weighting neighbourhood and a weighting kernel for each pixel location. It essentially consists in a weighted averaging combining both spatial and tonal information, for which a twofold similarity measure has to be calculated from local geodesic time functions. By designing relevant geodesic masks, two adaptive filtering algorithms are derived, that are particularly efficient at smoothing heterogeneous areas while preserving relevant structures in greyscale and multichannel images.|
|JRC Institute:||Institute for Environment and Sustainability|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.