pp. 2687-2696
S&M2293 Research Paper of Special Issue https://doi.org/10.18494/SAM.2020.2798 Published: August 20, 2020 Application of Tsallis Cross-entropy in Image Thresholding Segmentation [PDF] Qian-Qian Lin, Ling Zhang, Tung-Lung Wu, Tean-Shine Mean, and Hsien-Wei Tseng (Received January 6, 2020; Accepted July 8, 2020) Keywords: image segmentation, Tsallis cross-entropy, long-range correlation
We propose a novel Tsallis cross-entropy thresholding method based on the premise that local long-range correlations rather than global long-range correlations may exist among the gray levels of pixels. The target and background of the image can be considered as two independent parts, and their information integrity can be maximized by the proposed method. Our experimental results show that this method can obtain better segmentation results than the minimum Tsallis cross-entropy thresholding method when considering global long-range correlations when segmenting images in which the object and background have no obvious correlations.
Corresponding author: Tung-Lung Wu, Hsien-Wei TsengThis work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Qian-Qian Lin, Ling Zhang, Tung-Lung Wu, Tean-Shine Mean, and Hsien-Wei Tseng, Application of Tsallis Cross-entropy in Image Thresholding Segmentation, Sens. Mater., Vol. 32, No. 8, 2020, p. 2687-2696. |