NDLI: An improved Fuzzy C-means algorithm based on gray-scale histogram for underwater image segmentation

Content Provider	IEEE Xplore Digital Library
Author	Wang Shi-Long Wan Lei Tang Xu-Dong
Copyright Year	2010
Description	Author affiliation: National Key Laboratory of Science and Technology on Autonomous Underwater Vehicle, Harbin Engineering University, 150001, China (Wang Shi-Long; Wan Lei; Tang Xu-Dong)
Abstract	Due to the assimilation of the water and uneven lightness, the underwater images would have low S/N and the detail is fuzzy. If traditional methods are used to dispose underwater images directly, it is unlikely to obtain satisfactory results. Though traditional Fuzzy C-means algorithm could sometimes divide the image into object and background, its time-consuming computation is often an obstacle. As the mission of the vision system of autonomous underwater vehicle (AUV), it should deal with the information about the object in the complex environment rapidly and exactly for AUV to use the obtained result for the next task. So, aiming at realizing a clustering quickly on the basis of providing a high qualified segmentation of an underwater image, a novel fuzzy C-means algorithm based on gray-scale histogram for underwater image segmentation is proposed. Experimental results indicate that the novel algorithm can get a better segmentation result and the processing time of each image is reduced and enhance efficiency and satisfy the request of highly real-time effectiveness of AUV.
Starting Page	2778
Ending Page	2783
File Size	604428
Page Count	6
File Format	PDF
ISBN	9781424462636
e-ISBN	9787894631046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Association of Aut
Subject Keyword	Real time systems Gold Underwater Image Real-time Effectiveness Image Segmentation Gray-scale Autonomous Underwater Vehicle (AUV) Underwater vehicles Image segmentation Histograms Fuzzy C-means Clustering algorithms Gray-scale Histogram
Content Type	Text
Resource Type	Article

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