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- W2349142743 abstract "Due to the assimilation of the water and uneven lightness,the underwater image would have low S/N and the edge is fuzzy.If the traditional methods based on one-dimensional histogram are used to dispose it directly,the result is not expected.The two-dimensional maximum entropy method not only considers the distribution of the gray information,but also takes advantage of the spatial neighbor information derived from the two-dimensional histogram of the images.As a global threshold method,it often gets ideal segmentation results.However,its time-consuming computation is often an obstacle in real time application systems.In this paper,the edge detection approach based on the particle swarm optimization algorithm and the two-dimensional grayscale histogram is proposed to deal with underwater image.In the proposed method,the particle swarm optimization algorithm is realized successfully in the process of searching the optimal solution(s,t) in the two-dimensional grayscale space.Then the optimal solution(s,t),where s is a threshold for pixel intensity and t is another threshold for the local average intensity of pixels,is selected as the threshold of edge detection operators.The experiments of segmenting the underwater images are illustrated to show that the proposed method is effective.Comparing with the traditional methods,this approach shows better adaptive and noise restraining performance." @default.
- W2349142743 created "2016-06-24" @default.
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- W2349142743 date "2007-01-01" @default.
- W2349142743 modified "2023-09-25" @default.
- W2349142743 title "Edge detection based on two-dimensional histogram and particle swarm optimization" @default.
- W2349142743 hasPublicationYear "2007" @default.
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