Intelligent Visual Perception Tutorial智能视感学 (英文版)(普通高等教育“十二五”规划双语系列教材)

Foreword
Preface
    Base article
Chapter 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 Seing
    1.2.2  Image Acquisition
    1.2.3  PC Hardware Requirements for VPS
  Exercises
Chapter 2  Foundatio 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 Traformation
    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
  Exercises
Chapter 3  Mathematical Model of the Camera
  3.1  Geometric Traformatio of Image Space
    3.1.1   Homogeneous Coordinates
    3.1.2  Orthogonal Traformation and Rigid Body Traformation
    3.1.3  Similarity Traformation and Affine Traformation
    3.1.4  Pepective Traformation
  3.2  Image Coordinate System and Its Traformation
    3.2.1  Image Coordinate System
    3.2.2  Image Coordinate Traformation
  3.3  Common Method of Calibration Camera Paramete
    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
  Exercises
Chapter 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  Cotruction 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
  Exercises
Chapter 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  Teor Geometry Principle
    5.3.2  Geometric Properties of Three Visual Teor
    5.3.3  Operation of Three-visual Teor
    5.3.4  Cotraint Matching Feature Points of Three-visual Teor
    5.3.5  Three-visual Teor Restrict the Three Visual Restraint Feature Line' s Matching
  Exercises
  Application article
Chapter 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 Pedestria
  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
  Exercises
Appendix
  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 Equatio
    I.2.2  Least Square Solution of Nonlinear Homogeneous System of Equatio 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 Risk
III  Statistical Learning and VC-dimeion Theorem
  III.1  Bounding Theory and VC-dimeion Principle
  III.2  Generalized Capability Bounding
  III.3  Structural Risk Minimization Principle of Induction
IV  Optimality Conditio on Cotrained 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 Condition
Subject Index
References