Contour Extraction Based on Adaptive Thresholding in Sonar Images

Antonios Andreatos, Apostolos Leros
<span title="2021-08-30">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dmr4kpn2yreovpdxpdiqtjcrnu" style="color: black;">Information</a> </i> &nbsp;
A common problem in underwater side-scan sonar images is the acoustic shadow generated by the beam. Apart from that, there are a number of reasons impairing image quality. In this paper, an innovative algorithm improving contour extraction is presented. Contour extraction is based on automatically estimating the optimal threshold for converting the original gray scale images into binary images. The proposed algorithm clears the shadows and masks most of the impairments in side-scan sonar
more &raquo; ... The idea is to select a proper threshold towards the rightmost local minimum of the histogram, i.e., closest to the white values. For this purpose, the histogram envelope is approximated by two alternative contour extraction methods: polynomial curve fitting and data smoothing. Experimental results indicate that the proposed algorithm produces superior results than popular thresholding methods and common edge detection filters, even after corrosion expansion. The algorithm is simple, robust and adaptive and can be used in automatic target recognition, classification and storage in large-scale multimedia databases.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/info12090354">doi:10.3390/info12090354</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mzcps7nhbzhlpdjqucr6nfhneq">fatcat:mzcps7nhbzhlpdjqucr6nfhneq</a> </span>
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