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基于灰度—梯度共生矩阵模型的最大熵阈值处理算法
引用本文:周德龙,申石磊,蒲小勃,潘泉,张洪才.基于灰度—梯度共生矩阵模型的最大熵阈值处理算法[J].小型微型计算机系统,2002,23(2):136-138.
作者姓名:周德龙  申石磊  蒲小勃  潘泉  张洪才
作者单位:1. 西北工业大学,自动控制系,陕西,西安,710072
2. 河南大学,河南,开封,475001
3. 北京航空航天大学,北京,100083
摘    要:阈值法是图像分割的一种重要方法,在图像处理与识别中广为应用。本文提出了基于灰度-梯度共生矩阵模型和是大熵原理的灰度图像的自动阈值化技术,该方法不仅利用了图像的灰度信息,而且也利用了图像的梯度信息。该方法通过计算基于灰度-梯度共生矩阵的二维熵并使边缘区域的熵最大来选择阈值向量。仿真结果显示该算法比一维熵方法效果更佳。

关 键 词:阈值  灰度-梯度共生矩阵  图像分割  最大熵阈值处理算法  图像处理  图像识别
文章编号:1000-1220(2002)02-0136-03

Maximum Entropy Thresholding Algorithm Based on the Gray Level-Gradient Co-ocurrence Matrix
ZHOU De long ,SHEN Shi lei ,PU Xiao bo ,PAN Quan ,ZHANG Hong cai.Maximum Entropy Thresholding Algorithm Based on the Gray Level-Gradient Co-ocurrence Matrix[J].Mini-micro Systems,2002,23(2):136-138.
Authors:ZHOU De long  SHEN Shi lei  PU Xiao bo  PAN Quan  ZHANG Hong cai
Affiliation:ZHOU De long 1,SHEN Shi lei 2,PU Xiao bo 3,PAN Quan 1,ZHANG Hong cai 1 1
Abstract:Thresholding is an important form of image segmentation and is used in the processing of image for many applications. In this paper, we present an automatic technique for thresholding of digital images based on gray level gradient co occurrence matrix and the maximum entropy principle. This method attempts to utilize the information of both gray level and gradient in an image, the present approach evaluates two dimensional entropies based on the gray level gradient co occurrence matrix. The 2D threshold vector that maximizes the edge class entropies is selected. It is found that the proposed approach performs better by comparing the results with those of one dimensional entropic methods.
Keywords:threshold  entropy  gray level  gradient co  occurrence matrix  image segmentation
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