应用Gabor滤波器和局部边缘概率直方图的全局纹理方向性度量

赵海英; 冯月萍

doi:10.3788/OPE.20101807.1668

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应用Gabor滤波器和局部边缘概率直方图的全局纹理方向性度量

信息科学 | 更新时间：2020-08-12

- 应用Gabor滤波器和局部边缘概率直方图的全局纹理方向性度量
- Metric of global texture direction with histogram of local edge probability and Gabor filter
- 光学精密工程 2010年18卷第7期页码：1668-1674
- 作者机构：
  
  1. 北京科技大学信息工程学院北京,100083
  2. 吉林大学计算机学院,吉林长春,130012
  3. 新疆师范大学数理信息学院,新疆乌鲁木齐,830054
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.60863010)()
- DOI：10.3788/OPE.20101807.1668
  中图分类号：
- 收稿日期：2009-09-09，
  
  修回日期：2009-10-26，
  
  网络出版日期：2010-07-30，
  
  纸质出版日期：2010-07-20
- 稿件说明：
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赵海英, 冯月萍. 应用Gabor滤波器和局部边缘概率直方图的全局纹理方向性度量[J]. 光学精密工程, 2010,18(7): 1668-1674

ZHAO Hai-ying, FENG Yue-ping. Metric of global texture direction with histogram of local edge probability and Gabor filter[J]. 光学精密工程, 2010,18(7): 1668-1674
赵海英, 冯月萍. 应用Gabor滤波器和局部边缘概率直方图的全局纹理方向性度量[J]. 光学精密工程, 2010,18(7): 1668-1674 DOI： 10.3788/OPE.20101807.1668.

ZHAO Hai-ying, FENG Yue-ping. Metric of global texture direction with histogram of local edge probability and Gabor filter[J]. 光学精密工程, 2010,18(7): 1668-1674 DOI： 10.3788/OPE.20101807.1668.

摘要

为了构建人机识别纹理方向性模型

提出了一种图像全局纹理方向性的度量算法。首先

设计Gabor滤波器以减小次要方向分量

增强描述纹理的全局方向性分量。然后

构造局部边缘概率直方图

描述纹理方向性的整体分布

定量给出全局方向性度量值。最后

在Tamura纹理模型实验集以及Brodatz纹理图像库上

测试比较了用不同测度算法度量全局纹理方向性的精度。结果显示

在两类纹理图像库上所获得的测试值均与人类视觉感知保持了较高的一致性

其精度比其它算法可高出10%

表明论文所提的全局纹理方向性度量算法可以满足精度要求更高的人机纹理识别模型。

Abstract

In order to construct a reference model of human-machine identification texture direction

a metric algorithm for global texture direction is proposed.Firstly

a Gabor filter is designed to reduce the component less important direction and enhance that of global direction for description texture.Then

the histogram of local edge probability is constructed to describe the distribution of global texture and to give a quantitative value of measurement. Finally

the proposed algorithm is applied to comparing the accuracy of two experimental data sets from Tamura Texture Model and Brodatz texture database. Results indicate that the accuracy of the algorithm is well consistent with that of human visual perception measurement

and it is higher 10% than that of other algorithms.The algorithm can satisfy the requirements of a better reference model of human-machine identification texture direction for the higher precision.

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Keywords

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