TY - GEN
T1 - Automatic Detection of Underwater Objects in Sonar Imagery
AU - Abu, Avi
AU - DIamant, Roee
N1 - Publisher Copyright: © 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - 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).
AB - 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).
KW - Sonar image processing
KW - binary hypothesis testing
KW - detection in sonar imagery
KW - highlight detection
KW - image segmentation
KW - likelihood ratio test
UR - https://www.scopus.com/pages/publications/85103855141
U2 - 10.1109/OCEANSE.2019.8867489
DO - 10.1109/OCEANSE.2019.8867489
M3 - Conference contribution
T3 - OCEANS 2019 - Marseille, OCEANS Marseille 2019
BT - OCEANS 2019 - Marseille, OCEANS Marseille 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 OCEANS - Marseille, OCEANS Marseille 2019
Y2 - 17 June 2019 through 20 June 2019
ER -