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Open AccessArticle

Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram

by Xiulian Zheng 1,†, Hong Ye 1,† and Yinggan Tang 2,*,†
1
Department of Electric Automation Technology, College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, Zhejiang, China
2
Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2017, 19(5), 191; https://doi.org/10.3390/e19050191
Received: 22 March 2017 / Revised: 21 April 2017 / Accepted: 24 April 2017 / Published: 26 April 2017
(This article belongs to the Section Information Theory, Probability and Statistics)
Thresholding is a popular method of image segmentation. Many thresholding methods utilize only the gray level information of pixels in the image, which may lead to poor segmentation performance because the spatial correlation information between pixels is ignored. To improve the performance of thresolding methods, a novel two-dimensional histogram—called gray level-local variance (GLLV) histogram—is proposed in this paper as an entropic thresholding method to segment images with bimodal histograms. The GLLV histogram is constructed by using the gray level information of pixels and its local variance in a neighborhood. Local variance measures the dispersion of gray level distribution of pixels in a neighborhood. If a pixel’s gray level is close to its neighboring pixels, its local variance is small, and vice versa. Therefore, local variance can reflect the spatial information between pixels. The GLLV histogram takes not only the gray level, but also the spatial information into consideration. Experimental results show that an entropic thresholding method based on the GLLV histogram can achieve better segmentation performance. View Full-Text
Keywords: image segmentation; thresholding; Shannon entropy; gray level-local variance histogram image segmentation; thresholding; Shannon entropy; gray level-local variance histogram
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Zheng, X.; Ye, H.; Tang, Y. Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram. Entropy 2017, 19, 191.

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