FN Archimer Export Format
PT J
TI Region based variational approach for the segmentation textured sonar images
OT Segmentation texturales des images sonar des fonds marins par une approche variationnelle basée région
BT 
AF KAROUI, Imen
   FABLET, Ronan
   BOUCHER, Jean-Marc
   AUGUSTIN, Jean-Marie
AS 1:1;2:2;3:1;4:2;
FF 1:;2:PDG-DOP-DCB-STH-LASAA;3:;4:PDG-DOP-DCB-NSE-AS;
C1 CNRS TAMCIC, ENST Bretagne, GET, F-29238 Brest, France.
   IFREMER, TSI STH, F-29280 Plouzane, France.
C2 CNRS, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-DOP-DCB-STH-LASAA
   PDG-DOP-DCB-NSE-AS
IN WOS Ifremer jusqu'en 2018
   copubli-france
IF 0.121
TC 2
UR https://archimer.ifremer.fr/doc/2008/publication-6120.pdf
LA English
DT Article
DE ;Level sets;Active regions;Segmentation;Angular backscattering;Feature selection;Sonar images;Texture
AB 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.
PY 2008
PD DEC
SO Traitement du signal
SN 0765-0019
PU GRETSI
VL 25
IS 1-2
UT 000257023800007
BP 73
EP 85
ID 6120
ER

EF