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Pregled bibliografske jedinice broj: 640375

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Autori: Tai Fei; Dieter Kraus; Ivan Aleksi
Naslov: An Expectation-Maximization Approach Applied to Underwater Target Detection
( An Expectation-Maximization Approach Applied to Underwater Target Detection )
Izvornik: Proceedings of the International Conference on Detection and Classification of Underwater Targets (DCUT), Brest, 2012. / Isabelle Quidu, Vincent Myers, Benoit Zerr (ur.). - Newcastle, United Kingdom : Cambridge Scholars Publishing , 2014. (ISBN: 978-1-4438-5709-3).
Skup: ICoURS’12 – International Conference on Underwater Remote Sensing
Mjesto i datum: Brest, Francuska, 8-11.10.2012.
Ključne riječi: image segmentation; Pearson distribution system; Dempster-Shafer evidence theory; expectationmaximization algorithm; synthetic aperture sonar
( image segmentation; Pearson distribution system; Dempster-Shafer evidence theory; expectationmaximization algorithm; synthetic aperture sonar )
Sažetak:
In this paper, an expectation-maximization (EM) approach assisted by Dempster-Shafer evidence theory (DST) for image segmentation is presented. The likelihood function for EM approach proposed by Sanjay-Gopal et al., which decouples the spatial correlation between pixels far away from each other, is taken into account. The Gaussian mixture model is extended to a generalized mixture model which adopts the Pearson distribution system, so that our approach can approximate the statistics of the sonar imagery with more flexibility. Moreover, an intermediate step (I-step) based on DST is introduced between the E- and M-steps of the EM to consider the spatial dependency among neighboring pixels. Finally, numerical tests are carried out on SAS images. Our approach is quantitatively compared to those methods from the literature with the help of se veral evaluation measures for image egmentation.
Vrsta sudjelovanja: Predavanje
Vrsta prezentacije u zborniku: Cjeloviti rad (više od 1500 riječi)
Vrsta recenzije: Međunarodna recenzija
Projekt / tema: 165-0362980-2002, 165-0361621-2000
Izvorni jezik: eng
Kategorija: Znanstveni
Znanstvena područja:
Računarstvo
Upisao u CROSBI: Ivan Aleksi (Ivan.Aleksi@etfos.hr), 2. Ruj. 2013. u 10:09 sati



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