纹理图像分割算法的研究
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I 纹理图像分割算法的研究
中文摘要
图像分割是图像处理与计算机视觉领域中最为基础和重要的问题之一，它是对图
像进行视觉分析和模式识别的基本前提，图像分割的效果将直接影响到后续分析、识
别和解译等处理。纹理是图像的重要特征，普遍存在于各类图像当中，由于纹理图像
自身的复杂性，使得纹理图像的分割显得尤为困难。
本文围绕纹理图像的分割技术和实现算法展开研究，主要工作有两方面：一是图
像纹理特征的表述和提取算法的研究，二是基于纹理特征的图像分割技术研究。论文
工作主要有以下几个方面：
1、系统地研究了图像纹理特征的数学描述方法、纹理特征的提取算法以及基于
纹理特征的图像分割方法，参考了大量的文献，并通过实验比较，确定选用灰度共生
矩阵，实验结果显示采用灰度共生矩阵提取图像纹理特征能得到更好的分割效果；
2、详细地分析了模糊聚类算法，为了改善聚类效果，对模糊C均值聚类算法进
行了改进，使用硬C均值初始聚类中心以缩短聚类时间。并采用本改进算法进行了
实验，结果显示该算法在改善分割效果和缩短分割时间方面都具有很好的效果；
3、研究了径向基神经网络和径向基概率神经网络，通过对比，最终采用径向基
概率神经网络对图像纹理特征进行分类。详细介绍了网络创建、参数设置以及网络训
练方法，实验证明使用径向基概率神经网络能实现灰度共生矩阵提取的纹理特征进行
很好的分类，获得较为满意的分割结果。
本论文介绍的方法是以纹理库中的图像为研究对象提出的，但基本原理、算法同
样适用于其它类型图像的分割
关键词：神经网络，模糊C-均值，纹理特征，灰度共生矩阵，图像分割
作 者：毛伟民
指导教师：赵勋杰
II The research of texture image segmentation
Abstract
The most important step of image processing is the image segmentation, because the
results of image segmentation can directly affect the precision of followed procedures. The
texture is one of the most important features to images, it can be found in every image.
Because of the complexity of texture, the segmentation of texture image is especially
difficult, and it seriously restricts the development of image processing.
Research was made on the texture image segmentation algorithm, the processing have
two parts, one is the texture features distill based on texture feature distilling algorithm,
The other one is get segmentation result by classifying texture features. The main work is
focused on these aspects.
1. Systematically studied the characteristics of mathematical algorithms of texture
image description, the texture feature extraction algorithm, as well as image segmentation
method based on texture features. After reading of a lot of disquisition and experiments,
gray level co-occurrence matrix were used to extract the texture features of images, after
that experiments were made，and the results of experiments show that the use of Gray
Level Co-occurrence matrix texture features extraction can get better segmentation results;
2. Made full researches on fuzzy clustering algorithm, especially the fuzzy C–mean
clustering. Proposed a new C–mean clustering algorithm. This algorithm uses modified
distance function, data pre-processing and cluster center initialization using Hard C-means
clustering algorithm, in order to improve the performance and reduce the clustering time.
3. Studied RBF and RBF possibility artificial neural network, by comparing, RBF
possibility neural network was chose, depicted the theory of RBF possibility neural
network, and presented the network building, parameter setting and network training
method. By experiments, the results show that the using of RBF possibility neural network
can receive impressive image segmentation results.
The algorithm is proposed to segment the texture images in texture image library, but
the essential theory is also the same with other types of images. It can be conveniently
applied in other image by modifying a small quantity of parameters.
III
Key Words：Neural network; Fuzzy c-mean; Texture feature; Gray level
Co-occurrence matrix (GLCM), Image segmentation
Written by: Mao Weimin
Supervised by: Zhao Xunjie
目 录
第1章 绪论........................................................................................................................1
1.1 论文研究的目的与意义.................................................................................................1
1.2 国内外的研究现状.........................................................................................................1
1.2.1纹理图像分析的研究现状...........................................................................1
1.2.2灰度共生矩阵研究现状...............................................................................3
1.3 论文的主要工作及安排.................................................................................................4
第2章 纹理的描述和分析方法..........................................................................................6
2.1 纹理的定义和描述.........................................................................................................6
2.2 纹理分析方法.................................................................................................................6
2.2.1 空间域纹理分析[61]
.....................................................................................6
2.2.2 基于频率域的纹理分析方法......................................................................7
2.2.3空间/频率域联合分析法.............................................................................8
2.2.4 分形纹理分析 [39]
........................................................................................9
2.3 基于灰度共生矩阵的纹理特征提取...........................................................................10
