, Volume 10, Issue 1, pp 70-75
Date: 02 Apr 2011

A fast underwater optical image segmentation algorithm based on a histogram weighted fuzzy c-means improved by PSO

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The S/N of an underwater image is low and has a fuzzy edge. If using traditional methods to process it directly, the result is not satisfying. Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background, its time-consuming computation is often an obstacle. The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task. So, by using the statistical characteristics of the gray image histogram, a fast and effective fuzzy C-means underwater image segmentation algorithm was presented. With the weighted histogram modifying the fuzzy membership, the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm, so as to speed up the efficiency of the segmentation, but also improve the quality of underwater image segmentation. Finally, particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above. It made up for the shortcomings that the FCM algorithm can not get the global optimal solution. Thus, on the one hand, it considers the global impact and achieves the local optimal solution, and on the other hand, further greatly increases the computing speed. Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced. They enhance efficiency and satisfy the requirements of a highly effective, real-time AUV.

Foundation item: Supported by the National Natural Science Foundation of China under Grant No.50909025/E091002 and the Open Research Foundation of SKLab AUV, HEU under Grant No.2008003.
Shilong Wang was born in 1980. He is a PhD candidate in the design and constrction of naval architecture at Harbin Engineering University. His current research interests include underwater image processing and pattern recognition, etc.
Yuru Xu was born in 1942. He is an academician of CAE and a professor at Harbin Engineering University. His current research interests include intelligent control of AUV and vision research and overall design, etc.
Yongjie Pang was born in 1955. He is a professor at Harbin Engineering University. His current research interests include overall design and simulation of AUV, etc.