Robust A*-Search Image Segmentation Algorithm for Mine-like Objects Segmentation in SONAR Images (CROSBI ID 279429)
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Podaci o odgovornosti
Aleksi, Ivan ; Matić, Tomislav ; Lehmann, Benjamin ; Kraus, Dieter
engleski
Robust A*-Search Image Segmentation Algorithm for Mine-like Objects Segmentation in SONAR Images
This paper addresses a sonar image segmentation method employing a Robust A*-Search Image Segmentation (RASIS) algorithm. RASIS is applied on Mine-Like Objects (MLO) in sonar images, where an object is defined by highlight and shadow regions, i.e. regions of high and low pixel intensities in a side-scan sonar image. RASIS uses a modified A*-Search method, which is usually used in mobile robotics for finding the shortest path where the environment map is predefined, and the start/goal locations are known. RASIS algorithm represents the image segmentation problem as a path-finding problem. Main modification concerning the original A*-Search is in the cost function that takes pixel intensities and contour curvature in order to navigate the 2D segmentation contour. The proposed method is implemented in Matlab and tested on real MLO images. MLO image dataset consist of 70 MLO images with manta mine present, and 70 MLO images with cylinder mine present. Segmentation success rate is obtained by comparing the ground truth data given by the human technician who is detecting MLOs. Measured overall success rate (highlight and shadow regions) is 91% for manta mines and 81% for cylinder mines.
A*-search ; image segmentation ; path planning ; synthetic aperture sonar
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Podaci o izdanju
11 (2)
2020.
53-66
objavljeno
1847-6996
1847-7003
10.32985/ijeces.11.2.1
Povezanost rada
Elektrotehnika, Računarstvo