{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T03:27:13Z","timestamp":1778902033009,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,4,26]],"date-time":"2017-04-26T00:00:00Z","timestamp":1493164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61273260"],"award-info":[{"award-number":["61273260"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Public Welfare Technology Research Project of Zhejiang Province","award":["2014C31021"],"award-info":[{"award-number":["2014C31021"]}]},{"name":"the Specialized Research Fund for the Doctoral Program of Higher Education of China","award":["20121333120010"],"award-info":[{"award-number":["20121333120010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2014called gray level-local variance (GLLV) histogram\u2014is 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/e19050191","type":"journal-article","created":{"date-parts":[[2017,4,26]],"date-time":"2017-04-26T13:42:06Z","timestamp":1493214126000},"page":"191","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram"],"prefix":"10.3390","volume":"19","author":[{"given":"Xiulian","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Electric Automation Technology, College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Electric Automation Technology, College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7440-1742","authenticated-orcid":false,"given":"Yinggan","family":"Tang","sequence":"additional","affiliation":[{"name":"Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ndteint.2011.10.008","article-title":"Automatic weld defect detection based on potential defect tracking in real-time radiographic image sequence","volume":"46","author":"Shao","year":"2012","journal-title":"NDT & E Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1016\/j.patcog.2009.09.006","article-title":"Defect detection of uneven brightness in low-contrast images using basis image representation","volume":"43","author":"Tsneg","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1016\/j.jvcir.2014.04.002","article-title":"CSM neural network for degraded printed character optical recognition","volume":"25","author":"Namane","year":"2014","journal-title":"J. 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