Multivariate optimization for multifractal-based texture segmentation
Résumé
This work aims at segmenting a texture into different regions, each characterized by a priori unknown multifractal properties. The multifractal properties are quantified using the multiscale function C 1,j that quantifies the evolution along analysis scales 2 j of the empirical mean of the log of the wavelet leaders. The segmentation procedure, applied on (C 1,j) 1≤j≤J , involves a multivariate Mumford-Shah relaxation formulated as a convex optimization problem involving a structure tensor penalization and an efficient algorithmic solution based on primal-dual proximal algorithm. The performances are evaluated over simulated data.
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