Can I read A Segmentation Framework for Acoustic Sidescan Sonar Images Using Improved Smallest Of Constant False Alarm Rate and MAP-MRF on EtoBox?

A Segmentation Framework for Acoustic Sidescan Sonar Images Using Improved Smallest Of Constant False Alarm Rate and MAP-MRF by Yiteng Tang; Jun Liu; Shanshan Song; Wenxue Guan; Jun-Hong Cui is a scholarly article available to read on EtoBox.

What is A Segmentation Framework for Acoustic Sidescan Sonar Images Using Improved Smallest Of Constant False Alarm Rate and MAP-MRF about?

Segmentation of sidescan sonar image is a significant issue in underwater object detection and recognition. However, most prior methods only consider segmentation accuracy, ignoring false alarm rate, which plays a vital role in object detection and recognition. In this paper, a robust and accurate segmentation framework for sidescan sonar image is proposed, which balances a preferred tradeoff between accuracy and false alarm rate. The proposed method integrates an improved Smallest Of Constant False Alarm Rate (SO-CFAR) algorithm and a Maximum A Posteriori probability and Markov Random Field model (MAP-MRF). The part of innovations segments acoustical highlight region accurately while preserving edge features, which can make segmentation results obtain preferred false alarm rate. After that, MAP-MRF is employed for overcoming drawbacks associated with higher threshold value in continuous acoustical highlight areas. Besides, to better deal with intensity inhomogeneity, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is incorporated into this method, which can locate Region Of Interest (ROI) in sonar images as well as improve

Author

Yiteng Tang; Jun Liu; Shanshan Song; Wenxue Guan; Jun-Hong Cui

Publisher

ACM

Published

2022

Language

EN