智能视感学-英文版 内容简介

本书从计算机视感及其信号处理的基本概念与基础理论出发，阐述了基于图像信息的识别、理解与检测技术原理与方法。本书根据作者多年来从事智能视感理论与技术研究成果，结合研究性本科与研究生教学特点编撰而成。全书分为基础篇与应用篇两大部分，其中，基础篇系统地介绍了智能视感的基本原理、方法、关键技术及其算法；应用篇则由配合主要基础理论和方法的应用技术实例所组成。全书遵循理论知识与实用技术的紧密结合、数学方法与实用效果的相互映证等编写原则。本书可以作为检测与控制、自动化、计算机、机器人及人工智能等专业的高年级本科生和研究生的教材，也可作为专业技术人员的参考工具书。

智能视感学-英文版 本书特色

《智能视感学（英文版）》作者张秀彬、曼苏乐根据自己和博士、硕士生们的研究成果，结合多年从事本科生及研究生的教学经验与体会整理出这本教材，将其定名为《智能视感学》。考虑到目前在该学科方向上尚缺乏较为浅显易懂、又能形成体系的简明教程，作者想做一次尝试，希望能用一种较为通俗和深入浅出的方法来阐述智能视感的一些深奥知识，对初学者能够起到入门和建立继续深造的起点之作用。这是一本基于图像信息的非接触式传感理论的技术书。教材中所阐述的内容涉及到图像识别、视差原理、计算几何原理、计算机图像图形学，乃至人类对自然界认识的诸多先验知识如何与视感检测相结合的方法和技术问题。因此，本书是一本多学科交叉的较为前沿的大学研究型教材。

智能视感学-英文版 目录

ForewordPrefaceBase articleChapter 1Introduction1.1Overview1.1.1Concept about the Visual Perception1.1.2The Development of Visual Perception Technology1.1.3Classification of Visual Perception System1.2A Visual Perception Hardware-base1.2.1 iImage Sensing1.2.2Image Acquisition1.2.3PC Hardware Requirements for VPSExercisesChapter 2Foundations of Image Processing2.1Basic Processing Methods for Gray Image2.1.1Spatial Domain Enhancement Algorithm2.1.2Frequency Domain Enhancement Algorithm2.2Edge Detection of Gray Image2.2.1Threshold Edge Detection2.2.2Gradient-based Edge Detection2.Z.3Laplacian Operator2.2.4Canny Edge Operator2.2.5Mathematical Morphological Method2.2.6Brief Description of Other Algorithms2.3Binarization Processing and Segmentation of Image2.3.1General Description2.3.2Histogram-based Valley-point Threshold Image Binarization2.3.3OTSU Algorithm2.3.4Minimum Error Method of Image Segmentation2.4Color Image Enhancement2.4.1Color Space and Its Transformation2.4.2Histogram Equalization of Color Levels in Color Image2.5Color Image Edge Detection2.5.1 Color Image Edge Detection Based on Gradient Extreme Value2.5.2Practical Method for Color Image Edge DetectionExercisesChapter 3Mathematical Model of the Camera3.1Geometric Transformations of Image Space3.1.1 Homogeneous Coordinates3.1.2Orthogonal Transformation and Rigid Body Transformation3.1.3Similarity Transformation and Affine Transformation3.1.4Perspective Transformation3.2Image Coordinate System and Its Transformation3.2.1Image Coordinate System3.2.2Image Coordinate Transformation3.3Common Method of Calibration Camera Parameters3.3.1Step Calibration Method3.3.2Calibration Algorithm Based on More than One Free Plane3.3.3Non-linear Distortion Parameter Calibration MethodExercisesChapter 4Visual Perception Identification Algorithms4.1Image Feature Extraction and Identification Algorithm4.1.1Decision Theory Approach4.1.2Statistical Classification Method4.1.3Feature Classification Discretion Similarity about the Image Recognition Process4.2Principal Component Analysis4.2.1Principal Component Analysis Principle4.2.2Kernel Principal Component Analysis4.2.3PCA-based Image Recognition4.3Support Vector Machines4.3.1 Main Contents of Statistical Learning Theory4.3.2Classification-Support Vector Machine~4.3.3Solution to the Nonlinear Regression Problem4.3.4Algorithm of Support Vector Machine4.3.5Image Characteristics Identification Based on SVM4.4Moment Invariants and Normalized Moments of Inertia4.4.1Moment Theory4.4.2Normalized Moment of Inertia4.5Template Matching and Similarity4.5.1Spatial Domain Description of Template Matching4.5.2Frequency Domain Description of Template Matching4.6Object Recognition Based on Color Feature4.6.1Image Colorimetric Processing4.6.2Construction of Color-Pool4.6.3Object Recognition Based on Color4.7Image Fuzzy Recognition Method4.7.1Fuzzy Content Feature and Fuzzy Similarity Degree4.7.2Extraction of Fuzzy Structure4.7.3Fuzzy Synthesis Decision-making of Image MatchingExercisesChapter 5Detection Principle of Visual Perception5.1Single View Geometry and Detection Principle of Monocular Visual Perception5.1.1Single Vision Coordinate System5.1.2Basic Algorithm for Single Vision Detection5.1.3Engineering Technology Based on Single View Geometry5.2Detection Principle of Binocular Visual Perception5.2.1Two-view Geometry and Detection of Binocular Perception5.2.2Epipolar Geometry Principle5.2.3Determination Method of Spatial Coordinates5.2.4Camera Calibration in Binocular Visual Perception System5.3Theoretical Basis for Multiple Visual Perception Detection5.3.1Tensor Geometry Principle5.3.2Geometric Properties of Three Visual Tensor5.3.3Operation of Three-visual Tensor5.3.4Constraint Matching Feature Points of Three-visual Tensor5.3.5Three-visual Tensor Restrict the Three Visual Restraint Feature Line' s MatchingExercisesApplication articleChapter 6Practical Technology of Intelligent Visual Perception6.1Automatic Monitoring System and Method of Load Limitation of The Bridge6.1.1The Basic Composition of The System6.1.2System Algorithm6.2Intelligent Identification System for Billet Number6.2.1System Control Program6.2.2Recognition Algorithm6.3Verification of Banknotes-Sorting Based on Image Information6.3.1Preprocessing of the Banknotes Image6.3.2Distinction Between Old and New Banknotes6.3.3Distinction of the Denomination and Direction of the Banknotes6.3.4Banknotes Fineness Detection6.4Intelligent Collision Avoidance Technology of Vehicle6.4.1Basic Hardware Configuration6.4.2Road Obstacle Recognition Algorithm6.4.3Smart Algorithm of Anti-collision to Pedestrians6.5Intelligent Visual Perception Control of Traffic Lights6.5.1Overview6.5.2The Core Algorithm of Intelligent Visual Perception Control of Traffic LightsExercisesAppendixLeast Square and Common Algorithms in Visual Perception DetectionI.1Basic Idea of the AlgorithmI.2Common Least Square Algorithms in Visual Perception DetectionI.2.1 Least Square of Linear System of EquationsI.2.2Least Square Solution of Nonlinear Homogeneous System of Equations Theory and Method of BAYES Decision II.1Introduction II.2BAYES Classification Decision Mode II.2.1BAYES Classification of Minimum Error Rate II.2.2BAYES Classification Decision of Minimum RiskIIIStatistical Learning and VC-dimension TheoremIII.1Bounding Theory and VC-dimension PrincipleIII.2Generalized Capability BoundingIII.3Structural Risk Minimization Principle of InductionIVOptimality Conditions on Constrained Nonlinear Programming ProblemIV.1Kuhn-Tucker ConditionIV.1.1Gordon LemmaIV.1.2Fritz John TheoremIV.1.3Proof of the Kuhn-Tucker ConditionIV.2Karush-Kuhn-Tucker ConditionSubject IndexReferences