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2006 First International Multi-Symposiums on Computer and Computational Sciences
Underwater Image Segmentation with Maximum Entropy based on Particle Swarm Optimization (PSO)
Hangzhou, Zhejiang, China
June 20-June 24
ISBN: 0-7695-2581-4
Rubo Zhang, Harbin Engineering University, China
Jing Liu, Harbin Engineering University, China
The contrast of the underwater images is often extraordinarily low due to the ray, assimilating of water, illuminating condition and so on. It is not good for the pretreatment like edge detection and image segmentation. The theory of entropy has been widely used in the pre-process of under water images. However the time-consuming computation is often an obstacle in real time application systems. In this paper, the image thresholding approach with the index of entropy maximization of the grayscale histogram based on a new optimization algorithm, namely, the particle swarm optimization (PSO) algorithm is proposed to deal with underwater image. The experiments of segmenting the underwater images are illustrated to show that the proposed method can get ideal segmentation result with less computation cost.
Index Terms:
Entropy, Underwater image, Image segmentation, Particle swarm optimization (PSO)
Citation:
Rubo Zhang, Jing Liu, "Underwater Image Segmentation with Maximum Entropy based on Particle Swarm Optimization (PSO)," imsccs, vol. 2, pp.360-636, 2006 First International Multi-Symposiums on Computer and Computational Sciences, 2006
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