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- W3143683310 abstract "Aiming at the problem of inaccurating image segmentation caused by image noise and the common threshold selection methods which only rely on the probabilistic information from the image histogram is without directly thinking of the uniformity of the image inter-class gray distribution, a threshold selection algorithm based on a three-dimensional histogram correction and gray entropy decomposition is proposed. It analyzes the influence of image noise to the gray of pixel's neighborhood region, and reduces the noise interference by modifying the three-dimensional histogram. A formula of threshold selection based on three-dimensional gray entropy is presented, and the dimension of gray entropy is decomposed to one dimension, which makes the computation complexity reduced from O(L3) to O(L). Experimental results show that, compared with two-dimensional maximum entropy algorithm based on oblique segmentation, two-dimensional cross entropy algorithm based on recursion and three-dimensional Otsu algorithm based on dimension reduction, the presented algorithm has better anti-noise performance, visual quality and the operation time is reduced by about 10% at least." @default.
- W3143683310 created "2021-04-13" @default.
- W3143683310 creator A5004469476 @default.
- W3143683310 date "2014-01-01" @default.
- W3143683310 modified "2023-09-27" @default.
- W3143683310 title "Image Segmentation Based on Three-dimensional Histogram Correction and Gray Entropy Decomposition" @default.
- W3143683310 hasPublicationYear "2014" @default.
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