Intelligent Visual Perception Tutorial智能視感學(xué)

內(nèi)容概要

張秀彬、曼蘇樂編著的《智能視感學(xué)（英文版）》從計(jì)算機(jī)視感及其信號(hào)處理的基本概念與基礎(chǔ)理論出發(fā)，闡述了基于圖像信息的識(shí)別、理解與檢測技術(shù)原理與方法。
本書根據(jù)作者多年來從事智能視感理論與技術(shù)研究的成果，結(jié)合研究性本科與研究生教學(xué)特點(diǎn)編撰而成。全書分為基礎(chǔ)篇與應(yīng)用篇兩大部分，其中，基礎(chǔ)篇系統(tǒng)地介紹了智能視感的基本原理、方法、關(guān)鍵技術(shù)及其算法；應(yīng)用篇?jiǎng)t由配合主要基礎(chǔ)理論和方法的應(yīng)用技術(shù)實(shí)例所組成。全書遵循理論知識(shí)與實(shí)用技術(shù)的緊密結(jié)合、數(shù)學(xué)方法與實(shí)用效果的相互映證等編寫原則。本書涉及的教學(xué)內(nèi)容主要包括：圖像處理基礎(chǔ)、攝像機(jī)數(shù)學(xué)模型、視感識(shí)別與檢測原理、智能視感實(shí)用技術(shù)等。
《智能視感學(xué)（英文版）》可以作為檢測與控制、自動(dòng)化、計(jì)算機(jī)、機(jī)器人及人工智能等專業(yè)的高年級(jí)本科生和研究生的教材，也可作為專業(yè)技術(shù)人員的參考工具書。

書籍目錄

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

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