@inproceedings{53f09629d5e242d58695a1bf28caed91,
title = "Unsupervised segmentation of underwater objects in sonar images",
abstract = "We focus on the segmentation of sonar images for the aim of underwater object detection. Speckle noise and intensity inhomogeneity may cause false detections, and complex seabed textures like sand-ripples and sea-grass often lead to false segmentation. To tackle these problems, we propose a new method to incorporate the possible spatial correlation between neighboring pixels in the sonar image for improved segmentation. Our method modifies the expectation-maximization (EM) algorithm by adding an intermediate step (I-step) between the expectation (E-step) and maximization (M-step). Results show that our proposed method achieves improved segmentation performance over the state-of-the-art and is also robust to different seabed texture and for intensity inhomogeneity.",
keywords = "Expectation-maximization (EM), gamma distribution, object detection, sand ripples, sonar image segmentation, speckle noise",
author = "Avi Abu and Roee Diamant",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; OCEANS 2017 - Aberdeen ; Conference date: 19-06-2017 Through 22-06-2017",
year = "2017",
month = oct,
day = "25",
doi = "https://doi.org/10.1109/OCEANSE.2017.8084853",
language = "American English",
series = "OCEANS 2017 - Aberdeen",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "OCEANS 2017 - Aberdeen",
address = "United States",
}