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ForewordPreface Base articleChapter 1 Introduction 1.1 Overview 1.1.1 Concept about the Visual Perception 1.1.2 The Development of Visual Perception Technology 1.1.3 Classification of Visual Perception System 1.2 A Visual Perception Hardware-base 1.2.1 iImage Sensing 1.2.2 Image Acquisition 1.2.3 PC Hardware Requirements for VPS ExercisesChapter 2 Foundations of Image Processing 2.1 Basic Processing Methods for Gray Image 2.1.1 Spatial Domain Enhancement Algorithm 2.1.2 Frequency Domain Enhancement Algorithm 2.2 Edge Detection of Gray Image 2.2.1 Threshold Edge Detection 2.2.2 Gradient-based Edge Detection 2.Z.3 Laplacian Operator 2.2.4 Canny Edge Operator 2.2.5 Mathematical Morphological Method 2.2.6 Brief Description of Other Algorithms 2.3 Binarization Processing and Segmentation of Image 2.3.1 General Description 2.3.2 Histogram-based Valley-point Threshold Image Binarization 2.3.3 OTSU Algorithm 2.3.4 Minimum Error Method of Image Segmentation 2.4 Color Image Enhancement 2.4.1 Color Space and Its Transformation 2.4.2 Histogram Equalization of Color Levels in Color Image 2.5 Color Image Edge Detection 2.5.1 Color Image Edge Detection Based on Gradient Extreme Value 2.5.2 Practical Method for Color Image Edge Detection ExercisesChapter 3 Mathematical Model of the Camera 3.1 Geometric Transformations of Image Space 3.1.1 Homogeneous Coordinates 3.1.2 Orthogonal Transformation and Rigid Body Transformation 3.1.3 Similarity Transformation and Affine Transformation 3.1.4 Perspective Transformation 3.2 Image Coordinate System and Its Transformation 3.2.1 Image Coordinate System 3.2.2 Image Coordinate Transformation 3.3 Common Method of Calibration Camera Parameters 3.3.1 Step Calibration Method 3.3.2 Calibration Algorithm Based on More than One Free Plane 3.3.3 Non-linear Distortion Parameter Calibration Method ExercisesChapter 4 Visual Perception Identification Algorithms 4.1 Image Feature Extraction and Identification Algorithm 4.1.1 Decision Theory Approach 4.1.2 Statistical Classification Method 4.1.3 Feature Classification Discretion Similarity about the Image Recognition Process 4.2 Principal Component Analysis 4.2.1 Principal Component Analysis Principle 4.2.2 Kernel Principal Component Analysis 4.2.3 PCA-based Image Recognition 4.3 Support Vector Machines 4.3.1 Main Contents of Statistical Learning Theory 4.3.2 Classification-Support Vector Machine ~ 4.3.3 Solution to the Nonlinear Regression Problem 4.3.4 Algorithm of Support Vector Machine 4.3.5 Image Characteristics Identification Based on SVM 4.4 Moment Invariants and Normalized Moments of Inertia 4.4.1 Moment Theory 4.4.2 Normalized Moment of Inertia 4.5 Template Matching and Similarity 4.5.1 Spatial Domain Description of Template Matching 4.5.2 Frequency Domain Description of Template Matching 4.6 Object Recognition Based on Color Feature 4.6.1 Image Colorimetric Processing 4.6.2 Construction of Color-Pool 4.6.3 Object Recognition Based on Color 4.7 Image Fuzzy Recognition Method 4.7.1 Fuzzy Content Feature and Fuzzy Similarity Degree 4.7.2 Extraction of Fuzzy Structure 4.7.3 Fuzzy Synthesis Decision-making of Image Matching ExercisesChapter 5 Detection Principle of Visual Perception 5.1 Single View Geometry and Detection Principle of Monocular Visual Perception 5.1.1 Single Vision Coordinate System 5.1.2 Basic Algorithm for Single Vision Detection 5.1.3 Engineering Technology Based on Single View Geometry 5.2 Detection Principle of Binocular Visual Perception 5.2.1 Two-view Geometry and Detection of Binocular Perception 5.2.2 Epipolar Geometry Principle 5.2.3 Determination Method of Spatial Coordinates 5.2.4 Camera Calibration in Binocular Visual Perception System 5.3 Theoretical Basis for Multiple Visual Perception Detection 5.3.1 Tensor Geometry Principle 5.3.2 Geometric Properties of Three Visual Tensor 5.3.3 Operation of Three-visual Tensor 5.3.4 Constraint Matching Feature Points of Three-visual Tensor 5.3.5 Three-visual Tensor Restrict the Three Visual Restraint Feature Line' s Matching Exercises Application articleChapter 6 Practical Technology of Intelligent Visual Perception 6.1 Automatic Monitoring System and Method of Load Limitation of The Bridge 6.1.1 The Basic Composition of The System 6.1.2 System Algorithm 6.2 Intelligent Identification System for Billet Number 6.2.1 System Control Program 6.2.2 Recognition Algorithm 6.3 Verification of Banknotes-Sorting Based on Image Information 6.3.1 Preprocessing of the Banknotes Image 6.3.2 Distinction Between Old and New Banknotes 6.3.3 Distinction of the Denomination and Direction of the Banknotes 6.3.4 Banknotes Fineness Detection 6.4 Intelligent Collision Avoidance Technology of Vehicle 6.4.1 Basic Hardware Configuration 6.4.2 Road Obstacle Recognition Algorithm 6.4.3 Smart Algorithm of Anti-collision to Pedestrians 6.5 Intelligent Visual Perception Control of Traffic Lights 6.5.1 Overview 6.5.2 The Core Algorithm of Intelligent Visual Perception Control of Traffic Lights ExercisesAppendix Least Square and Common Algorithms in Visual Perception Detection I.1 Basic Idea of the Algorithm I.2 Common Least Square Algorithms in Visual Perception Detection I.2.1 Least Square of Linear System of Equations I.2.2 Least Square Solution of Nonlinear Homogeneous System of Equations Theory and Method of BAYES Decision II.1 Introduction II.2 BAYES Classification Decision Mode II.2.1 BAYES Classification of Minimum Error Rate II.2.2 BAYES Classification Decision of Minimum RiskIII Statistical Learning and VC-dimension Theorem III.1 Bounding Theory and VC-dimension Principle III.2 Generalized Capability Bounding III.3 Structural Risk Minimization Principle of InductionIV Optimality Conditions on Constrained Nonlinear Programming Problem IV.1 Kuhn-Tucker Condition IV.1.1 Gordon Lemma IV.1.2 Fritz John Theorem IV.1.3 Proof of the Kuhn-Tucker Condition IV.2 Karush-Kuhn-Tucker ConditionSubject IndexReferences