@inproceedings{6ed11ae306534d248810b8b79aaeb247,
title = "Automatic Detection of Underwater Objects in Sonar Imagery",
abstract = "This paper introduces a new unsupervised statistically-based algorithm for the detection of underwater objects in sonar imagery. Highlights are detected by a higher-order-statistics representation of the image followed by a segmentation process to form a region-of-interest (ROI). Our algorithm sets its main parameters in situ and avoids the need of parameter calibration. Moreover, we do not require knowledge about the target's shape or size, thereby making our algorithm robust to any sonar detection application. Results obtained from real sonar system show a good trade-off between probability of detection and false alarm rate (FAR).",
keywords = "Sonar image processing, binary hypothesis testing, detection in sonar imagery, highlight detection, image segmentation, likelihood ratio test",
author = "Avi Abu and Roee DIamant",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 OCEANS - Marseille, OCEANS Marseille 2019 ; Conference date: 17-06-2019 Through 20-06-2019",
year = "2019",
month = jun,
doi = "10.1109/OCEANSE.2019.8867489",
language = "American English",
series = "OCEANS 2019 - Marseille, OCEANS Marseille 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "OCEANS 2019 - Marseille, OCEANS Marseille 2019",
address = "United States",
}