{"refrec":{"BRefID":247367,"RR":"<b>Lopera, O.; Dupont, Y.</b> (2013). Target classification from HR sonar images, <b><i>in</i></b>: IEEE <i>OCEANS 2013 Norway. Proceedings of a meeting held 10-14 June 2013, Bergen, Norway. Oceans (New York),</i> CFP13OCF: pp. 6 pp. <a href=\"https://dx.doi.org/10.1109/OCEANS-Bergen.2013.6608183\" target=\"_blank\">https://dx.doi.org/10.1109/OCEANS-Bergen.2013.6608183</a>","BEntID":239068,"PublicFlag":1,"CheckedFlag":0,"wosflag":0,"vabbflag":null,"RefStringPartII":", <b><i>in</i></b>: IEEE <i>OCEANS 2013 Norway. Proceedings of a meeting held 10-14 June 2013, Bergen, Norway. Oceans (New York),</i> CFP13OCF: pp. 6 pp. <a href=\"https://dx.doi.org/10.1109/OCEANS-Bergen.2013.6608183\" target=\"_blank\">https://dx.doi.org/10.1109/OCEANS-Bergen.2013.6608183</a>","DocTypID":17,"DocType":"Book chapters","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Lopera, O.; Dupont, Y.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Lopera, O.; Dupont, Y.","Englishabstract":"This paper presents two integrated techniques for target classification from high-resolution (HR) sonar images. Both recognition procedures start with a despeckling algorithm based on the anisotropic diffusion filter. As a second step, a fuzzymorpho-based segmentation procedure is applied to the filtered images, which partitions the image into highlights and shadow areas. A number of geometrical features are extracted from these areas, and are then used to classify targets using two techniques: (i) a Markov Chain Monte Carlo (MCMC) approach and (ii) a Decision Tree Classifier (DTC) . A comparison of both recognition techniques is drawn, and classification performance is estimated by ROC curves. 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