By virtue of two-dimension histogram, many classical thresholding methods had been extended to two dimensional case, such as two-dimension Otsu thresholding method, two-dimension Tsallis entropy thresholding method. The two-dimension histogram can reflect the contextual information between pixels to a certain extent. To solve this problem, Abutaleb proposed the concept of two-dimension histogram. However, these classical thresholding segmentation methods and their variants take only the brightness information into account and neglect the contextual information between pixels, which may result in poor segmentation performance or even false segmentation. These classical thresholding methods have some improved variants. The Otsu thresholding method selects the ideal threshold by maximizing the between-class variance between background and objects, and Kapur’s thresholding method maximizing the total Shannon entropy of background and objects, the Kittler’s thresholding method minimizing the classification error. The ideal thresholds locate at valleys and can be obtained by optimizing a certain criteria function. Thresholding methods assume that there is a deep valley between two peaks in the gray level histogram of the image. They become popular and have received much attention of researchers. Among different image segmentation methods, thresholding segmentation methods are simple, effective and more easy to be implemented. In practice, simple and effective segmentation methods are desirable. The clustering based methods, regression based methods, and deep learning based methods are the new and sophisticated methods.Īlthough the above methods can obtain well segmentation performance, however, the computation complexity and computation burden are relatively high. Image segmentation is an active research topic and many segmentation methods had been proposed up to now. The main purpose of image segmentation is to categorize an image’s pixels to different classes according to color, texture and brightness, etc. Image segmentation is a fundamental task in many computer vision based applications, such as medical image analysis, crack detection, video analysis, plant disease recognition, etc. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The specific roles of these authors are articulated in the ‘author contributions’ section.Ĭompeting interests: State GRID Quzhou Power Supply Company is the employer of author Wei Yang. The commercial affiliation State GRID Quzhou Power Supply Company provided support in the form of salaries for author Wei Yang, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper.įunding: This work was sponsored in part by Public Welfare Project of Zhejiang Province of China (LGG19F050003) to LC. Received: NovemAccepted: FebruPublished: March 3, 2020Ĭopyright: © 2020 Yang et al. PLoS ONE 15(3):īeijing University of Posts and Telecommunications, CHINA Citation: Yang W, Cai L, Wu F (2020) Image segmentation based on gray level and local relative entropy two dimensional histogram.
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