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Provedor de dados:  ArchiMer
País:  France
Título:  Region based variational approach for the segmentation textured sonar images
Autores:  Karoui, Imen
Fablet, Ronan
Boucher, Jean-marc
Augustin, Jean-marie
Data:  2008-12
Ano:  2008
Palavras-chave:  Level sets
Active regions
Segmentation
Angular backscattering
Feature selection
Sonar images
Texture
Resumo:  We propose a new region-based segmentation of textured sonar images with respect to seafloor types. We characterize sea-floor types by a set of empirical distributions estimated on texture responses to a set of different filters and we introduce a novel similarity measure between sonar textures in this attribute space. Our similarity measure is defined as a weighted sum of Kullback-Leibler divergences between texture features. The texture similarity measure weight setting is twofold: first we weight each filter, according to its discrimination power, the computation of these weights are issued from the margin maximization criterion, Second, we add an additional weighting, evaluated as an angular distance between the incidence angles of the compared texture samples, to cope with the problem related to the sonar image acquisition process that leads to a variability of the backscattered (BS) value and the texture aspect with the incidence angle range, Our segmentation method is stated as the minimization of a region-based functional that involves the similarity between region texture based statistics and prototype ones and a regularization term that imposes smoothness and regularity on region boundaries. The proposed approach is implemented using level-set methods, and the functional minimization is done using shape derivative tools.
Tipo:  Text
Idioma:  Inglês
Identificador:  http://archimer.ifremer.fr/doc/2008/publication-6120.pdf
Editor:  GRETSI
Relação:  http://archimer.ifremer.fr/doc/00000/6120/
Formato:  application/pdf
Fonte:  Traitement du signal (0765-0019) (GRETSI), 2008-12 , Vol. 25 , N. 1-2 , P. 73-85
Direitos:  2008 GRETSI, Saint Martin d'Hères, France
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