2 February 2012 Textured areas detection and segmentation in circular harmonic functions domain
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Abstract
In this work a novel technique for detecting and segmenting textured areas in natural images is presented. The method is based on the circular harmonic function, and, in particular, on the Laguerre Gauss functions. The detection of the textured areas is performed by analyzing the mean, the mode, and the skewness of the marginal densities of the Laguerre Gauss coefficients. By using these parameters a classification of the patch and of the pixel, is performed. The feature vectors representing the textures are built using the parameters of the Generalized Gaussian Densities that approximate the marginal densities of the Laguerre Gauss functions computed at three different resolutions. The feature vectors are clustered by using the K-means algorithm in which the symmetric Kullback-Leibler distance is adopted. The experimental results, obtained by using a set of natural images, show the effectiveness of the proposed technique.
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Luca Costantini, Luca Costantini, Licia Capodiferro, Licia Capodiferro, Marco Carli, Marco Carli, Alessandro Neri, Alessandro Neri, } "Textured areas detection and segmentation in circular harmonic functions domain", Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 829504 (2 February 2012); doi: 10.1117/12.908123; https://doi.org/10.1117/12.908123
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