课程门户-章节详情

机器视觉与图像处理

许吉禅

第1章概述(Introduction)
- ● 1.1 数字图像处理及特点(Characteristics and Processing of Digital Image)
  - ● 1.1.1 数字图像与数字图像处理(Digital Images and Digital Image Processing)
  - ● 1.1.2 数字图像处理的特点(Characteristics of Digital Image Processing)
- ● 1.2 数字图像处理系统(System of Digital Image Processing)
  - ● 1.2.1 数字图像处理系统的结构(Structure of Digital Image Processing System)
  - ● 1.2.2 数字图像处理的优点(Advantages of Digital Image Processing)
- ● 1.3 数字图像处理的主要研究内容(Research Content in Digital Image Processing)
- ● 1.4 数字图像处理的应⽤和发展(Applications and Development of Digital Image Processing)
  - ● 1.4.1 数字图像处理的应⽤(Applications of Digital Image Processing)
  - ● 1.4.2 数字图像处理领域的发展动向(Future Direction in the Field of Digital Image Processing)
- ● 1.5 全书内容简介(Brief Introduction of This Book)
- ● 第1章概述PPT
- ● 习题(Exercises)
第2章数字图像处理的基础(Basics of Digital Image Processing)
- ● 2.1 ⼈类的视觉感知系统(Visual System of Human Beings)
  - ● 2.1.1 视觉系统的基本构造(Basic Structure of Visual System)
  - ● 2.1.2 亮度适应和鉴别(Intensity Adaption and Identification)
- ● 2.2 数字图像的基础知识(Basics of Digital Image)
  - ● 2.2.1 图像的数字化及表达(Image Digitalization and Representation)
  - ● 2.2.2 图像的获取(Image Acquisition)
  - ● 2.2.3 像素间的基本关系(Basic Relationships between Pixels)
  - ● 2.2.4 图像的分类(Image Classification)
- ● 第2章数字图像处理的基础PPT
- ● 习题(Exercises)
第3章图像基本运算(Basic Operation in Digital Image Processing)
- ● 3.1 概述(Introduction)
- ● 3.2 点运算(Point Operation)
  - ● 3.2.1 线性点运算(Linear Point Operation)
  - ● 3.2.2 ⾮线性点运算(Non-Linear Point Operation)
- ● 3.3 代数运算与逻辑运算(Algebra and Logical Operation)
  - ● 3.3.1 加法运算(Addition)
  - ● 3.3.2 减法运算(Subtraction)
  - ● 3.3.3 乘法运算(Multiplication)
  - ● 3.3.4 除法运算(Division)
  - ● 3.3.5 逻辑运算(Logical Operation)
- ● 3.4 ⼏何运算(Geometric Operation)
  - ● 3.4.1 图像的平移(Image Translation)
  - ● 3.4.2 图像的镜像(Image Mirror)
  - ● 3.4.3 图像的旋转(Image Rotation)
  - ● 3.4.4 图像的缩放(Image Zoom)
  - ● 3.4.5 灰度重采样(Gray Resampling)
- ● 第3章图像基本运算PPT
- ● 习题(Exercises)
第4章图像变换(Image Transform)
- ● 4.1 连续傅⾥叶变换(Continuous Fourier Transform)
- ● 4.2 离散傅⾥叶变换(Discrete Fourier Transform)
- ● 4.3 快速傅⾥叶变换(Fast Fourier Transform)
- ● 4.4 傅里叶变换的性质(Properties of Fourier Transform)
  - ● 4.4.1 可分离性(Separability)
  - ● 4.4.2 平移性质(Translation)
  - ● 4.4.3 周期性和共轭对称性(Periodicity and Conjugate Symmetry)
  - ● 4.4.4 旋转性质(Rotation)
  - ● 4.4.5 分配律(Distribution Law)
  - ● 4.4.6 尺度变换(Scaling)
  - ● 4.4.7 平均值(Average Value)
  - ● 4.4.8 卷积定理(Convolution Theorem)
- ● 4.5 图像傅⾥叶变换实例(Examples of Image Fourier Transform)
- ● 4.6 其他离散变换(Other Discrete Transform)
  - ● 4.6.1 离散余弦变换(Discrete Cosine Transform)
  - ● 4.6.2 ⼆维离散沃尔什—哈达玛变换(Walsh-Hadamard Transform)
  - ● 4.6.3 卡胡楠—列夫变换(Kahunen-Loeve Transform)
- ● 第4章图像变换PPT
- ● 习题(Exercises)
第5章图像增强(Image Enhancement)
- ● 5.1 图像增强的概念和分类(Concepts and Categories of Image Enhancement)
- ● 5.2 空间域图像增强(Image Enhancement in the Spatial Domain)
  - ● 5.2.1 基于灰度变换的图像增强(Image Enhancement based on Gray Levels)
  - ● 5.2.2 基于直方图处理的图像增强(Image Enhancement based on Histogram Processing)
  - ● 5.2.3 空间域滤波增强(Spatial Filtering Enhancement)
- ● 5.3 频率域图像增强(Image Enhancement in the Frequency Domain)
  - ● 5.3.1 频率域增图像强基本理论(Fundamentals of Image Enhancement in the Frequency Domain)
  - ● 5.3.2 频率域平滑滤波器(Frequency Smoothing Filters)
  - ● 5.3.3 频率域锐化滤波器(Frequency Sharpening Filters)
  - ● 5.3.4 同态滤波器(Homomorphic Filters)
- ● 第5章图像增强PPT
- ● 习题(Exercises)
第6章图像复原(Image Restoration)
- ● 6.1 图像复原及退化模型基础(Fundamentals of Image Restoration and Degradation Model)
  - ● 6.1.1 图像退化的原因及退化模型(Causes of Image Degradation and Degradation Model)
  - ● 6.1.2 图像退化的数学模型(Mathematic Model of Image Degradation)
  - ● 6.1.3 复原技术的概念及分类(Concepts and Categories of Restoration)
- ● 6.2 噪声模型(Noise Models)
  - ● 6.2.1 ⼀些重要噪声的概率密度函数(Some Important Noise Probability Density Functions)
  - ● 6.2.2 噪声参数的估计(Estimation of Noise Parameters)
- ● 6.3 空间域滤波复原(Restoration with Spatial Filtering)
  - ● 6.3.1 均值滤波器(Mean Filters)
  - ● 6.3.2 顺序统计滤波器(Order-Statistics Filters)
- ● 6.4 频率域滤波复原(Restoration with Frequency Domain Filtering)
  - ● 6.4.1 带阻滤波器(Bandreject Filters)
  - ● 6.4.2 带通滤波器(Bandpass Filters)
  - ● 6.4.3 其他频率域滤波器(Other Filters in Frequency Domain)
- ● 6.5 估计退化函数(Estimating the Degradation Function)
  - ● 6.5.1 图像观察估计法(Estimation by Image Observation)
  - ● 6.5.2 试验估计法(Estimation by Experimentation)
  - ● 6.5.3 模型估计法(Estimation by Modeling)
- ● 6.6 逆滤波(Inverse Filtering)
- ● 6.7 最⼩均⽅误差滤波——维纳滤波(Minimum Mean Square Error Filtering-Wiener Filtering)
- ● 6.8 ⼏何失真校正(Geometric Distortion Correction)
  - ● 6.8.1 空间变换(Spatial Transformation)
  - ● 6.8.2 灰度插值(Gray-Level Interpolation)
  - ● 6.8.3 实现(Implementation)
- ● 第6章图像复原PPT
- ● 习题(Exercises)
第7章图像压缩编码(Image Compression Coding Technology)
- ● 7.1 概述(Introduction)
  - ● 7.1.1 图像的信息量与信息熵(Information Content and Entropy)
  - ● 7.1.2 图像数据冗余(Image Data Redundancy)
  - ● 7.1.3 图像压缩编码⽅法(Coding Methods of Image Compression)
  - ● 7.1.4 图像压缩技术的性能指标(Evaluation Index of Image Compression Approaches)
  - ● 7.1.5 保真度准则(Fidelity Criteria)
- ● 7.2 ⽆失真图像压缩编码(Lossless Image Compression)
  - ● 7.2.1 哈夫曼编码(Huffman Coding)
  - ● 7.2.2 游程编码(Run-Length Coding)
  - ● 7.2.3 算术编码(Arithmetic Coding)
- ● 7.3 有限失真图像压缩编码(Lossy Image Compression)
  - ● 7.3.1 率失真函数(Rate Distortion Function)
  - ● 7.3.2 预测编码和变换编码(Prediction Coding and Transform Coding)
  - ● 7.3.3 ⽮量量化编码(Vector Quantification Coding)
- ● 7.4 图像编码新技术(New Image Coding Technology)
  - ● 7.4.1 ⼦带编码(Subband Coding)
  - ● 7.4.2 模型基编码(Model-Based Coding)
  - ● 7.4.3 分形编码(Fractal Coding)
- ● 7.5 图像压缩技术标准(Image Compression Standards)
  - ● 7.5.1 概述(Introduction)
  - ● 7.5.2 JPEG压缩(JPEG Compression)
  - ● 7.5.3 JPEG 2000
  - ● 7.5.4 H.26x标准(H.26x Standards)
  - ● 7.5.5 MPEG标准(MPEG Standards)
- ● 第7章图像压缩编码PPT
- ● 习题(Exercises)
第8章图像分割(Image Segmentation)
- ● 8.1 概述(Introduction)
- ● 8.2 边缘检测和连接(Edge Detection and Connection)
  - ● 8.2.1 边缘检测(Edge Detection)
  - ● 8.2.2 边缘连接(Edge Connection)
- ● 8.3 阈值分割(Image Segmentation using Threshold)
  - ● 8.3.1 基础(Foundation)
  - ● 8.3.2 全局阈值(Global Threshold)
  - ● 8.3.3 ⾃适应阈值(Adaptive Threshold)
  - ● 8.3.4 最佳阈值的选择(Optimal Threshold)
  - ● 8.3.5 分⽔岭算法(Watershed Algorithm)
- ● 8.4 区域分割(Region Segmentation)
  - ● 8.4.1 区域⽣长法(Region Growing)
  - ● 8.4.2 区域分裂合并法(Region Splitting and Merging)
- ● 8.5 ⼆值图像处理(Binary Image Processing)
  - ● 8.5.1 数学形态学图像处理(Mathematical Morphology Image Processing)
  - ● 8.5.2 开运算和闭运算(Open Operation and Close Operation)
  - ● 8.5.3 ⼀些基本形态学算法(Some Basic Morphological Algorithms)
- ● 8.6 分割图像的结构(Construction of Image Segmentation)
  - ● 8.6.1 物体⾪属关系图(Relationships between Objects)
  - ● 8.6.2 边界链码(Edge Chain Code)
- ● 第8章图像分割PPT
- ● 习题(Exercises)
第9章彩⾊图像处理(Color Image Processing)
- ● 9.1 彩⾊图像基础(Fundamentals of Color Image)
  - ● 9.1.1 彩⾊图像的概念(Concepts of Color Image)
  - ● 9.1.2 彩⾊基础(Color Fundamentals)
- ● 9.2 彩⾊模型(Color Models)
  - ● 9.2.1 RGB彩⾊模型(RGB Color Model)
  - ● 9.2.2 CMY彩⾊模型和CMYK彩⾊模型(CMY Color model and CMYK Color Model)
  - ● 9.2.3 HIS彩⾊模型(HSI Color Model)
- ● 9.3 伪彩⾊处理(Pseudocolor Image Processing)
  - ● 9.3.1 背景(Background)
  - ● 9.3.2 强度分层(Intensity Slicing)
  - ● 9.3.3 灰度级到彩⾊变换(Transformation of Gray Levels to Color)
  - ● 9.3.4 假彩⾊处理(False-Color Image Processing)
- ● 9.4 全彩⾊图像处理(Full-Color Image Processing)
  - ● 9.4.1 全彩⾊图像处理基础(Basics of Full-Color Image Processing)
  - ● 9.4.2 彩⾊平衡(Color Balance)
  - ● 9.4.3 彩⾊图像增强(Color Image Enhancement)
  - ● 9.4.4 彩⾊图像平滑(Color Image Smoothing)
  - ● 9.4.5 彩⾊图像锐化(Color Image Sharpening)
- ● 9.5 彩⾊图像分割(Color Image Segmentation)
  - ● 9.5.1 HSI彩⾊空间分割(Segmentation in HSI Color Space)
  - ● 9.5.2 RGB彩⾊空间分割(Segmentation in RGB Color Space)
  - ● 9.5.3 彩⾊边缘检测(Color Edge Detection)
- ● 9.6 彩⾊图像处理的应⽤(Applications of Color Image Processing)
- ● 第9章彩⾊图像处理PPT
- ● 习题(Exercises)
第10章图像表⽰与描述(Image Representation and Description)
- ● 10.1 背景(Background)
- ● 10.2 颜⾊特征(Color Feature)
  - ● 10.2.1 灰度特征(Intensity Feature)
  - ● 10.2.2 直⽅图特征(Histogram Feature)
  - ● 10.2.3 颜⾊矩(Color Moments)
- ● 10.3 纹理特征(Representation of Image Texture)
  - ● 10.3.1 ⾃相关函数(Autocorrelation Function)
  - ● 10.3.2 灰度差分统计(Statistics of Intensity Difference)
  - ● 10.3.3 灰度共⽣矩阵(Gray-Level Co-occurrence Matrix)
  - ● 10.3.4 频谱特征(Spectrum Features)
- ● 10.4 边界特征(Boundary Feature)
  - ● 10.4.1 边界表达(Boundary Representation)
  - ● 10.4.2 边界特征描述(Boundary Description)
- ● 10.5 区域特征(Region Feature)
  - ● 10.5.1 简单的区域描述(Simple Region Descriptors)
  - ● 10.5.2 拓扑描述(Topological Descriptors)
  - ● 10.5.3 形状描述(Shape Descriptors)
  - ● 10.5.4 矩(Moment)
- ● 10.6 运⽤主成分进⾏描述(Use of Principal Components for Description)
  - ● 10.6.1 主成分基础(Fundamentals of Principal Components Analysis)
  - ● 10.6.2 主成分描述(Description by Principal Components Analysis)
- ● 10.7 特征提取的应⽤(Application of Feature Extraction)
  - ● 10.7.1 粒度测定(Granularity Mensuration)
  - ● 10.7.2 圆形⽬标判别(Circle Shape Recognition)
  - ● 10.7.3 运动⽬标特征提取(Feature Extraction of Moving Object)
- ● 第10章图像表⽰与描述PPT
- ● 习题(Exercises)
拓展内容
- ● 机器视觉基础知识
- ● 摄像机标定
- ● 图像拼接技术
机器视觉与图像处理应用案例
- ● MATLAB案例库
- ● Python案例库
MATLAB基础
- ● MATLAB软件安装
- ● MATLAB介绍
- ● MATLAB基本语法
- ● Simulink
MATLAB基本应用
- ● MATLAB绘图
- ● MATLAB矩阵运算
数字图像MATLAB处理
- ● 图像读取显示保存
- ● 图像类型转换颜色空间转换代数运算
实验一：图像变换Simulink实现
- ● 实验一图像变换的simulink实现1
- ● 实验一图像变换的simulink实现2
实验二：图像增强平滑锐化
- ● 图像增强Simulink实验
- ● 图像平滑Simulink实验
- ● 图像锐化Simulink实验
实验三：数学形态学
- ● 二值图像膨胀腐蚀开闭运算
- ● 二值图像形态学滤波
- ● 二值图像形态学平滑边界提取
- ● 灰度图像膨胀腐蚀开闭运算
- ● 灰度图像形态学平滑提取边界
- ● TophatBottonhat变换
- ● 边缘检测
实验四：图像处理
- ● 付立叶余弦变换
- ● 非线性灰度增强
- ● 膨胀腐蚀性质

6.7 最⼩均⽅误差滤波——维纳滤波(Minimum Mean Square Error Filtering-Wiener Filtering)

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