基于机器视觉的纹理识别

论文编号:DZXX236  论文字数:13151,页数:28

摘要

 纹理是一种很重要的视觉信息, 通过对物件表面纹理的分析，可以让计算机识别出该物件所属的材料。因此图像纹理识别成为数字图像识别的一个重要分支。纹理识别在织物纹理自动检测、医学图像分析、场景分析和遥感图像处理等领域中的广泛应用，进一步证明纹理识别的重大研究意义和实用价值。通过比较各种算法的精度及其功能实现的复杂程度，本文最后采用统计法中的灰度共生矩阵法实现纹理识别功能。灰度共生矩阵法，是建立在估计图像的二阶组合条件概率密度函数的数学模型基础之上。在VC++编译环境下，先通过灰度图计算出4个不同角度的灰度共生矩阵，然后计算出每个像元对在灰度共生矩阵中的概率，最后根据能量，熵，惯性矩，相关，局部平稳的计算公式计算出这5个特征值。实验结果表明，以这种方法做出来的识别系统，能够较准确地计算出每个特征值，从而区别不同的材料。同时也证明了，灰度共生矩阵法是一种实践性较高的算法。

关键字：纹理，纹理识别，灰度共生矩阵，VC++

Abstract

 Texture is one of the most important visual information, which makes the computer recognizes the material of the objects by analyzing the surface texture of them. Therefore, image texture recognition becomes one of the key branches of digital image recognition. Texture recognition has been widely used in the fields of fabric texture automatic detection, medical image analysis and remote sensing image processing, which further proves its research importance and practical value. With comparisons of different methods and the complex degree of function, the thesis finally applies gray level co-occurrence matrix to functionalize the texture recognition. Gray level co-occurrence matrix method is based on the second-order conditional probability density function of the estimated image. Under the condition of Visual C++ compilation, firstly the computer computes the gray level co-occurrence matrix of 4 angles from the gray image. Then, it will compute the probability of every pair pixel in the gray level co-occurrence matrix. Finally, it will compute the 5 Characteristic values according to the formula of energy, entropy, inertia matrix, correlation, and local balance. According to the experiment, this recognized system of that method can precisely compute those five characteristic values leading to the distinguishing of different materials. At the same time, the gray level co-occurrence matrix is proved to be a precisely practical method.

Keywords: Texture, Texture recognition, Gray level co-occurrence matrix, VC++

目录

摘要 I
Abstract II
第1章  绪论 1
1.1 数字图像处理的定义及其发展历程 1
1.2 研究的背景，目的和意义 2
1.3 图像纹理信息的概念 3
1.4 纹理识别模型的分类和比较 4
1.5 纹理识别的应用领域 5
第2章 Microsoft Visual C++ 6.0 编译环境介绍 7
2.1 Visual C++ 6.0简介 7
2.2 Visual C++ 6.0的特点 7
2.3 Visual C++ 6.0的常用向导简介 8
2.4 MFC类库简介 9
第3章 纹理分析的概念和算法的实现 11
3.1 纹理的定义 11
3.2 纹理分析的方法 11
3.3 灰度共生矩阵法的算法原理 12
3.4 灰度共生矩阵算法的程序实现 15
3.5 模式识别的实现 16
3.6 实验结果分析 17
第4章 总结和展望 22
致谢 23
参考文献 24