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• W3159313141 endingPage "107089" @default.
• W3159313141 startingPage "107089" @default.
• W3159313141 abstract "Infrared pedestrian segmentation is a challenging problem in infrared image processing. Kaniadakis entropy thresholding method can segment the infrared image with long tail distribution histogram. However, it failed in images with noise and complex background. In this paper, a novel infrared pedestrian segmentation algorithm based on the two-dimensional Kaniadakis entropy thresholding is proposed to address this problem. First, in order to introduce the spatial information of pixels, the two-dimensional Kaniadakis entropy thresholding algorithm is proposed through extending the one-dimensional Kaniadakis entropy thresholding algorithm and using the two-dimensional histogram of image. The recursive formulas of the two-dimensional algorithm are also presented to improve the computation efficiency. Second, an intensity suppressed strategy is embedded in the optimal thresholds searching process of the two-dimensional Kaniadakis entropy thresholding algorithm to segment the pedestrian in infrared images with complex background. In the experiment, the proposed algorithm is compared with the state-of-the-art image segmentation methods on several infrared images with different backgrounds. The experimental results verify the effectiveness of the proposed method both qualitatively and quantitatively." @default.
• W3159313141 created "2021-05-10" @default.
• W3159313141 creator A5068538399 @default.
• W3159313141 creator A5075853682 @default.
• W3159313141 date "2021-08-01" @default.
• W3159313141 modified "2023-09-23" @default.
• W3159313141 title "Infrared pedestrian segmentation algorithm based on the two-dimensional Kaniadakis entropy thresholding" @default.
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• W3159313141 doi "https://doi.org/10.1016/j.knosys.2021.107089" @default.
• W3159313141 hasPublicationYear "2021" @default.
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