Image thresholding using Tsallis entropy
Image thresholding using Tsallis entropy
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DOI：
10.1016/j.patrec.2004.03.003
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发表时间：
2004-07-02
影响因子：
5.1
通讯作者：
de Albuquerque, MP
中科院分区：
文献类型：
--
作者：
de Albuquerque, MP;Esquef, IA;de Albuquerque, MP
图像分析通常是指以找到图像中呈现的对象为目标的图像处理。图像分割是图像自动分析中的一个关键问题。非广延熵是统计力学中的一个新发展，它引入了一个真实的量q作为描述长程相互作用、长时间记忆和分形结构的物理系统的参数。在图像处理中，最有效的图像分割技术之一是基于熵的阈值分割。该方法利用信息论中的Shannon熵，将灰度图像的直方图看作是一种概率分布。在本文中，Tsallis熵作为一个一般的熵形式主义的信息论。首次提出了基于非广延熵的图像阈值化方法，该方法考虑了图像类中存在的非加性信息量。给出了一些典型的结果来说明参数q在阈值化中的影响。(C)2004 Elsevier B.V.保留所有权利。
Image analysis usually refers to processing of images with the goal of finding objects presented in the image. Image segmentation is one of the most critical tasks in automatic image analysis. The nonextensive entropy is a recent development in statistical mechanics and it is a new formalism in which a real quantity q was introduced as parameter for physical systems that present long range interactions, long time memories and fractal-type structures. In image processing, one of the most efficient techniques for image segmentation is entropy-based thresholding. This approach uses the Shannon entropy originated from the information theory considering the gray level image histogram as a probability distribution. In this paper, Tsallis entropy is applied as a general entropy formalism for information theory. For the first time image thresholding by nonextensive entropy is proposed regarding the presence of nonadditive information content in some image classes. Some typical results are presented to illustrate the influence of the parameter q in the thresholding. (C) 2004 Elsevier B.V. All rights reserved.