 | 书 名: 机器视觉（英文版）
作 者: Ramesh Jain
出 版 社: 机械工业出版社
ISBN : 711112643
原 价: ￥59
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Preface Acknowledgments Introduction 1.1 Machine Vision 1.2 Relationships to Other Fields 1.3 Role of Knowledge 1.4 Image Geometry 1.4.1 Perspective Projection 1.4.2 Coordinate Systems 1.5 Sampling and Quantization 1.6 Image Definitions 1.7 Levels of Computation 1.7.1 Point Level 1.7.2 Local Level 1.7.3 Global Level 1.7.4 Object Level 1.8 Road Map 2 Binary Image Processing 2.1 Thresholding 2.2 Geometric Properties 2.2.1 Size 2.2.2 Position 2.2.3 Orientation 2.3 Projections 2.4 Run-Length Encoding 2.5 Binary Algorithms 2.5.1 Definitions 2.5.2 Component Labeling 2.5.3 Size Filter 2.5.4 Euler Number 2.5.5 Region Boundary 2.5.6 Area and Perimeter 2.5.7 Compactness 2.5.8 Distance Measures 2.5.9 Distance Transforms 2.5.10 Medial Axis 2.5.11 Thinning 2.5.12 Expanding and Shrinking 2.6 Morphological Operators 2.7 Optical Character Recognition 3 Regions 3.1 Regions and Edges 3.2 Region Segmentation 3.2.1 Automatic Thresholding 3.2.2 Limitations of Histogram Methods 3.3 Region Representation 3.3.1 Array Representation 3.3.2 Hierarchical Representations 3.3.3 Region Characteristic-Based Representations 3.3.4 Data Structures for Segmentation 3.4 Split and Merge 3.4.1 Region Merging 3.4.2 Removing Weak Edges 3.4.3 Region Splitting 3.4.4 Split and Merge 3.5 Region Growing 4 Image Filtering 4.1 Histogram Modification 4.2 Linear Systems 4.3 Linear Filters 4.4 Median Filter 4.5 Gaussian Smoothing 4.5.1 Rotational Symmetry 4.5.2 Fourier Transform Property 4.5.3 Gaussian Separability 4.5.4 Cascading Gaussians 4.5.5 Designing Gaussian Filters 4.5.6 Discrete Ganssian Filters 5 Edge Detection 5.1 Gradient 5.2 Steps in Edge Detection 5.2.1 Roberts Operator 5.2.2 Sobel Operator 5.2.3 Prewitt Operator 5.2.4 Comparison 5.3 Second Derivative Operators 5.3.1 Laplacian Operator 5.3.2 Second Directional Derivative 5.4 Laplacian of Gaussian 5.5 Image Approximation 5.6 Gaussian Edge Detection 5.6.1 Canny Edge Detector 5.7 Subpixel Location Estimation 5.8 Edge Detector Performance 5.8.1 Methods for Evaluating Performance 5.8.2 Figure of Merit 5.9 Sequential Methods 5.10 Line Detection 6 Contours 6.1 Geometry of Curves 6.2 Digital Curves 6.2.1 Chain Codes 6.2.2 Slope Representation 6.2.3 Slope Density Function 6.3 Curve Fitting 6.4 Polyline Representation 6.4.1 Polyline Splitting 6.4.2 Segment Merging 6.4.3 Split and Merge 6.4.4 Hop-Along Algorithm 6.5 Circular Arcs 6.6 Conic Sections 6.7 Spline Curves 6.8 Curve Approximation 6.8.1 Total Regression 6.8.2 Estimating Corners 6.8.3 Robust Regression 6.8.4 Hough Transform 6.9 Fourier DeScriptors 7 Texture 7.1 Introduction 7.2 Statistical Methods of Texture Analysis 7.3 Structural Analysis of Ordered Texture 7.4 Model-Based Methods for Texture Analysis 7.5 Shape from Texture 8 Optics 8.1 Lens Equation 8.2 Image Resolution 8.3 Depth of Field 8.4 View Volume 8.5 Exposure 9 Shading 9.1 Image Irradiance 9.1.1 Illumination 9.1.2 Reflectance 9.2 Surface Orientation 9.3 The Reflectance Map 9.3.1 Diffuse Reflectance 9.4 Shape from Shading 9.5 Photometric Stereo l0 Color 10.1 Color Physics 10.2 Color Terminology 10.3 Color Perception 10.4 Color Processing 10.5 Color Constancy 10.6 Discussion 11 Depth 11.1 Stereo Imaging 11.1.1. Cameras in Arbitrary Position and Orientation 11.2 Stereo Matching 11.2.1 Edge Matching 11.2.2 Region Correlation 11.3 Shape from X 11.4 Range Imaging 11.4.1 Structured Lighting 11.4.2 Imaging Radar 11.5 Active Vision 12 Calibration 12.1 Coordinate Systems 12.2 Rigid Body Transformations 12.2.1 Rotation Matrices 12.2.2 Axis of Rotation 12.2.3 Unit Quaternions 12.3 Absolute Orientation 12.4 Relative Orientation 12.5 Rectification 12.6 Depth from Binocular Stereo 12.7 Absolute Orientation with Scale 12.8 Exterior Orientation 12.8.1 Calibration Example 12.9 Interior Orientation 12.10 Camera Calibration 12.10.1 Simple Method for Camera Calibration 12.10.2 Affine Method for Camera Calibration 12.10.3 Nonlinear Method for Camera Calibration 12.11 Binocular Stereo Calibration 12.12 Active Triangulation 12.13 Robust Methods 12.14 Conclusions 13 Curves and Surfaces 13.1 Fields 13.2 Geometry of Curves 13.3 Geometry of Surfaces 13.3.1 Planes 13.3.2 Differential Geometry 13.4 Curve Representations 13.4.1 Cubic Spline Curves 13.5 Surface Representations 13.5.1 Polygonal Meshes 13.5.2 Surface Patches 13.5.3 Tensor-Product Surfaces 13.6 Surface Interpolation 13.6.1 Triangular Mesh Interpolation 13.6.2 Bilinear Interpolation 13.6.3 Robust Interpolation 13.7 Surface Approximation 13.7.1 Regression Splines 13.7.2 Variational Methods 13.7.3 Weighted Spline Approximation 13.8 Surface Segmentation 13.8.1 Initial Segmentation 13.8.2 Extending Surface Patches 13.9 Surface Registration 14 Dynamic Vision 14.1 Change Detection 14.1.1 Difference Pictures 14.1.2 Static Segmentation and Matching 14.2 Segmentation Using Motion 14.2.1 Time-Varying Edge Detection 14.2.2 Stationary Camera 14.3 Motion Correspondence 14.4 Image Flow 14.4.1 Computing Image Flow 14.4.2 Feature-Based Methods 14.4.3 Gradient-Based Methods 14.4.4 Variational Methods for Image Flow 14.4.5 Robust Computation of Image Flow 14.4.6 Information in Image Flow 14.5 Segmentation Using a Moving Camera 14.5.1 Ego-Motion Complex Log Mapping 14.5.2 Depth Determination 14.6 Tracking 14.6.1 Deviation Function for Path Coherence 14.6.2 Path Coherence Function 14.6.3 Path Coherence in the Presence of Occlusion 14.6.4 Modified Greedy Exchange Algorithm 14.7 Shape from Motion Object Recognition 15.1 System Components 15.2 Complexity of Object Recognition 15.3 Object Representation 15.3.1 Observer-Centered Representations 15.3.2 Object-Centered Representations 15.4 Feature Detection 15.5 Recognition Strategies 15.5.1 Classification 15.5.2 Matching 15.5.3 Feature Indexing 15.6 Verification 15.6.1 Template Matching 15.6.2 Morphological Approach 15.6.3 Symbolic 15.6.4 Analogical Methods A Mathematical Concepts A.1 Analytic Geometry A.2 Linear Algebra A.3 Variational Calculus A.4 Numerical Methods B Statistical Methods B.1 Measurement Errors B.2 Error Distributions B.3 Linear Regression B.4 Nonlinear Regression C Programming Techniques C.1 Image DeScriptors C.2 Mapping Operators C.3 Image File Formats Bibliography Index
机器视觉（英文版）-图书简介:
近年，?谌蛐畔⒒蟪钡耐贫拢夜募扑慊捣⒄寡该停宰ㄒ等瞬诺男枨笕找嫫惹小Ｕ舛约扑慊逃绾统霭娼缍技仁腔觯彩翘粽剑欢ㄒ到滩牡慕ㄉ柙诮逃铰陨舷缘镁僮闱嶂亍Ｔ谖夜畔⒓际醴⒄故奔浣隙獭⒋右等嗽苯仙俚南肿聪拢拦确⒋锕以谄浼扑慊蒲Х⒄沟募甘昙浠淼木浣滩娜杂行矶嘀档媒杓Α?
Ramesh Jain创建了密歇根大学的人工知识能实验室，目前是加利福尼亚大学圣迭戈分校电气和计算机工程、计算机科学和工程系的教授。他的研究方向是多媒体信息系统、图像数据库、机器视觉和智能系统。他是《IEEE Multimedia》杂志的主编，《Machine Vision and Application》、《Pattern Recognition》和《Image and Vision Computing》杂志编委会成员，还是IEEE和AAAL的特别会员，ACM的会员。
机器视觉领域的研究博大精深，而且日新月异，对于具体视觉应用系统的设计人员和用户来说，该从何着手呢？本书是机器视觉领域的一本入门教材，详细介绍了基本概念，并辅以必要的数学知识，用较大篇幅来讲解如何在实际应用中实现和使用视觉算法，同时强调了技术的工程层。本书有意省略了机器视视中某些没有充分实际应用的理论。
本书可以作为高校相关专业的教材，也适合希望应用机器视觉来解决实际问题的各类人员阅读。
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