Handbook of Image Engineering

Preface
General Directory
Contents
About the Author
Part I: Image Fundamentals
	Chapter 1: Image Basics
		1.1 Basic Concepts of Image
			1.1.1 Image and Image Space
			1.1.2 Digital Image and Computer-Generated Image
		1.2 Image Decomposition
			1.2.1 Image Decomposition
			1.2.2 Pixel and Voxel
			1.2.3 Various Elements
		1.3 All Kinds of Image
			1.3.1 Images with Different Wavelengths
			1.3.2 Different Dimensional Images
			1.3.3 Color Image
			1.3.4 Images for Different Applications
		1.4 Special Attribute Images
			1.4.1 Images with Various Properties
			1.4.2 Image with Specific Attribute
			1.4.3 Depth Images
			1.4.4 Image with Variant Sources
			1.4.5 Processing Result Image
			1.4.6 Others
		1.5 Image Representation
			1.5.1 Representation
			1.5.2 Image Property
			1.5.3 Image Resolution
		1.6 Image Quality
	Chapter 2: Image Engineering
		2.1 Image Engineering Technology
			2.1.1 Image Engineering
			2.1.2 Image Processing
			2.1.3 Image Analysis
			2.1.4 Image Understanding
		2.2 Similar Disciplines
			2.2.1 Computer Vision
			2.2.2 Machine Vision
			2.2.3 Computer Graphics
			2.2.4 Light Field
		2.3 Related Subjects
			2.3.1 Fractals
			2.3.2 Topology
			2.3.3 Virtual Reality
			2.3.4 Others
	Chapter 3: Image Acquisition Devices
		3.1 Device Parameters
			3.1.1 Camera Parameters
			3.1.2 Camera Motion Description
			3.1.3 Camera Operation
		3.2 Sensors
			3.2.1 Sensor Models
			3.2.2 Sensor Characteristics
			3.2.3 Image Sensors
			3.2.4 Specific Sensors
			3.2.5 Commonly Used Sensors
		3.3 Cameras and Camcorders
			3.3.1 Conventional Cameras
			3.3.2 Camera Models
			3.3.3 Special Structure Cameras
			3.3.4 Special Purpose Cameras
			3.3.5 Camera Systems
		3.4 Camera Calibration
			3.4.1 Calibration Basics
			3.4.2 Various Calibration Techniques
			3.4.3 Internal and External Camera Calibration
		3.5 Lens
			3.5.1 Lens Model
			3.5.2 Lens Types
			3.5.3 Lens Characteristics
			3.5.4 Focal Length of Lens
			3.5.5 Lens Aperture and Diaphragm
		3.6 Lens Aberration
			3.6.1 Lens Distortions
			3.6.2 Chromatic Aberration
		3.7 Other Equipment and Devices
			3.7.1 Input Devices
			3.7.2 Filters
			3.7.3 Microscopes
			3.7.4 RADAR
			3.7.5 Other Devices
	Chapter 4: Image Acquisition Modes
		4.1 Imaging and Acquisition
			4.1.1 Image Capture
			4.1.2 Field of View
			4.1.3 Camera Models
			4.1.4 Imaging Methods
			4.1.5 Spectral Imaging
			4.1.6 Coordinate Systems
			4.1.7 Imaging Coordinate Systems
			4.1.8 Focal Length and Depth
			4.1.9 Exposure
			4.1.10 Holography and View
		4.2 Stereo Imaging
			4.2.1 General Methods
			4.2.2 Binocular Stereo Imaging
			4.2.3 Special Methods
			4.2.4 Structured’Light
		4.3 Light Source and Lighting
			4.3.1 Light and Lamps
			4.3.2 Light Source
			4.3.3 Lighting
			4.3.4 Illumination
			4.3.5 Illumination’Field
		4.4 Perspective and Projection
			4.4.1 Perspective
			4.4.2 Perspective Projection
			4.4.3 Projective Imaging
			4.4.4 Various Projections
		4.5 Photography and Photogrammetry
			4.5.1 Photography
			4.5.2 Photogrammetry
	Chapter 5: Image Digitization
		5.1 Sampling and Quantization
			5.1.1 Sampling Theorem
			5.1.2 Sampling Techniques
			5.1.3 Quantization
		5.2 Digitization Scheme
			5.2.1 Digitization
			5.2.2 Digitizing Grid
	Chapter 6: Image Display and Printing
		6.1 Display
			6.1.1 Image Display
			6.1.2 Display Devices
		6.2 Printing
			6.2.1 Printing Devices
			6.2.2 Printing Techniques
			6.2.3 Halftoning Techniques
	Chapter 7: Image Storage and Communication
		7.1 Storage and Communication
			7.1.1 Image Storage
			7.1.2 Image Communication
		7.2 Image File Format
			7.2.1 Bitmap Images
			7.2.2 Various Formats
	Chapter 8: Related Knowledge
		8.1 Basic Mathematics
			8.1.1 Analytic and Differential Geometry
			8.1.2 Functions
			8.1.3 Matrix Decomposition
			8.1.4 Set Theory
			8.1.5 Least Squares
			8.1.6 Regression
			8.1.7 Linear Operations
			8.1.8 Complex Plane and Half-Space
			8.1.9 Norms and Variations
			8.1.10 Miscellaneous
		8.2 Statistics and Probability
			8.2.1 Statistics
			8.2.2 Probability
			8.2.3 Probability Density
			8.2.4 Probability Distributions
			8.2.5 Distribution Functions
			8.2.6 Gaussian Distribution
			8.2.7 More Distributions
		8.3 Signal Processing
			8.3.1 Basic Concepts
			8.3.2 Signal Responses
			8.3.3 Convolution and Frequency
		8.4 Tools and Means
			8.4.1 Hardware
			8.4.2 Software
			8.4.3 Diverse Terms
Part II: Image Processing
	Chapter 9: Pixel Spatial Relationship
		9.1 Adjacency and Neighborhood
			9.1.1 Spatial Relationship Between Pixels
			9.1.2 Neighborhood
			9.1.3 Adjacency
		9.2 Connectivity and Connected
			9.2.1 Pixel Connectivity
			9.2.2 Pixel-Connected
			9.2.3 Path
		9.3 Connected Components and Regions
			9.3.1 Image Connectedness
			9.3.2 Connected Region in Image
		9.4 Distance
			9.4.1 Discrete Distance
			9.4.2 Distance Metric
			9.4.3 Geodesic Distance
			9.4.4 Distance Transform
	Chapter 10: Image Transforms
		10.1 Transformation and Characteristics
			10.1.1 Transform and Transformation
			10.1.2 Transform Properties
		10.2 Walsh-Hadamard Transform
			10.2.1 Walsh Transform
			10.2.2 Hadamard Transform
		10.3 Fourier Transform
			10.3.1 Variety of Fourier Transform
			10.3.2 Frequency Domain
			10.3.3 Theorem and Property of Fourier Transform
			10.3.4 Fourier Space
		10.4 Discrete Cosine Transform
		10.5 Wavelet Transform
			10.5.1 Wavelet Transform and Property
			10.5.2 Expansion and Decomposition
			10.5.3 Various Wavelets
		10.6 Karhunen-Loève Transform
			10.6.1 Hotelling Transform
			10.6.2 Principal Component Analysis
		10.7 Other Transforms
	Chapter 11: Point Operations for Spatial Domain Enhancement
		11.1 Fundamentals of Image Enhancement
			11.1.1 Image Enhancement
			11.1.2 Intensity Enhancement
			11.1.3 Contrast Enhancement
			11.1.4 Operator
		11.2 Coordinate Transformation
			11.2.1 Spatial Coordinate Transformation
			11.2.2 Image Transformation
			11.2.3 Homogeneous Coordinates
			11.2.4 Hierarchy of Transformation
			11.2.5 Affine Transformation
			11.2.6 Rotation Transformation
			11.2.7 Scaling Transformation
			11.2.8 Other Transformation
		11.3 Inter-image Operations
			11.3.1 Image Operation
			11.3.2 Arithmetic Operations
			11.3.3 Logic Operations
		11.4 Image Gray-Level Mapping
			11.4.1 Mapping
			11.4.2 Contrast Manipulation
			11.4.3 Logarithmic and Exponential Functions
			11.4.4 Other Functions
		11.5 Histogram Transformation
			11.5.1 Histogram
			11.5.2 Histogram Transformation
			11.5.3 Histogram Modification
			11.5.4 Histogram Analysis
	Chapter 12: Mask Operations for Spatial Domain Enhancement
		12.1 Spatial Domain Enhancement Filtering
			12.1.1 Spatial Domain Filtering
			12.1.2 Spatial Domain Filters
		12.2 Mask Operation
			12.2.1 Mask
			12.2.2 Operator
		12.3 Linear Filtering
			12.3.1 Linear Smoothing
			12.3.2 Averaging and Mean
			12.3.3 Linear Sharpening
		12.4 Nonlinear Filtering
			12.4.1 Nonlinear Smoothing
			12.4.2 Mid-point, Mode, and Median
			12.4.3 Nonlinear Sharpening
		12.5 Gaussian Filter
			12.5.1 Gaussian
			12.5.2 Laplacian of Gaussian
	Chapter 13: Frequency Domain Filtering
		13.1 Filter and Filtering
			13.1.1 Basic of Filters
			13.1.2 Various Filters
		13.2 Frequency Domain Filters
			13.2.1 Filtering Techniques
			13.2.2 Low-Pass Filters
			13.2.3 High-Pass Filters
			13.2.4 Band-Pass Filters
			13.2.5 Band-Reject Filters
			13.2.6 Homomorphic Filters
	Chapter 14: Image Restoration
		14.1 Fundamentals of Image Restoration
			14.1.1 Basic Concepts
			14.1.2 Basic Techniques
			14.1.3 Simulated Annealing
			14.1.4 Regularization
		14.2 Degradation and Distortion
			14.2.1 Image Degradation
			14.2.2 Image Geometric Distortion
			14.2.3 Image Radiometric Distortion
		14.3 Noise and Denoising
			14.3.1 Noise Models
			14.3.2 Noise Sources
			14.3.3 Distribution
			14.3.4 Impulse Noise
			14.3.5 Some Typical Noises
			14.3.6 Image Denoising
		14.4 Filtering Restoration
			14.4.1 Unconstrained and Constrained
			14.4.2 Harmonic and Anisotropic
	Chapter 15: Image Repair and Recovery
		15.1 Image Inpainting
		15.2 Image Completion
		15.3 Smog and Haze Elimination
			15.3.1 Defogging and Effect
			15.3.2 Atmospheric Scattering Model
		15.4 Geometric Distortion Correction
			15.4.1 Geometric Transformation
			15.4.2 Grayscale Interpolation
			15.4.3 Linear Interpolation
	Chapter 16: Image Reconstruction from Projection
		16.1 Principle of Tomography
			16.1.1 Tomography
			16.1.2 Computational Tomography
			16.1.3 Historical Development
		16.2 Reconstruction Methods
		16.3 Back-Projection Reconstruction
		16.4 Reconstruction Based on Series Expansion
			16.4.1 Algebraic Reconstruction Technique
			16.4.2 Iterative Back-Projection
	Chapter 17: Image Coding
		17.1 Coding and Decoding
			17.1.1 Coding and Decoding
			17.1.2 Coder and Decoder
			17.1.3 Source coding
			17.1.4 Data Redundancy and Compression
			17.1.5 Coding Types
		17.2 Coding Theorem and Property
			17.2.1 Coding Theorem
			17.2.2 Coding Property
		17.3 Entropy Coding
			17.3.1 Entropy of Image
			17.3.2 Variable-Length Coding
		17.4 Predictive Coding
			17.4.1 Lossless and Lossy
			17.4.2 Predictor and Quantizer
		17.5 Transform Coding
		17.6 Bit Plane Coding
		17.7 Hierarchical Coding
		17.8 Other Coding Methods
	Chapter 18: Image Watermarking
		18.1 Watermarking
			18.1.1 Watermarking Overview
			18.1.2 Watermarking Embedding
			18.1.3 Watermarking Property
			18.1.4 Auxiliary Information
			18.1.5 Cover and Works
		18.2 Watermarking Techniques
			18.2.1 Technique Classification
			18.2.2 Various Watermarking Techniques
			18.2.3 Transform Domain Watermarking
		18.3 Watermarking Security
			18.3.1 Security
			18.3.2 Watermarking Attacks
			18.3.3 Unauthorized Attacks
	Chapter 19: Image Information Security
		19.1 Image Authentication and Forensics
			19.1.1 Image Authentication
			19.1.2 Image Forensics
		19.2 Image Hiding
			19.2.1 Information Hiding
			19.2.2 Image Blending
			19.2.3 Cryptography
			19.2.4 Other Techniques
	Chapter 20: Color Image Processing
		20.1 Colorimetry and Chromaticity Diagram
			20.1.1 Colorimetry
			20.1.2 Color Chart
			20.1.3 Primary and Secondary Color
			20.1.4 Color Mixing
			20.1.5 Chromaticity Diagram
			20.1.6 Diagram Parts
		20.2 Color Spaces and Models
			20.2.1 Color Models
			20.2.2 RGB-Based Models
			20.2.3 Visual Perception Models
			20.2.4 CIE Color Models
			20.2.5 Other Color Models
		20.3 Pseudo-color Processing
			20.3.1 Pseudo-color Enhancement
			20.3.2 Pseudo-Color Transform
		20.4 True Color Processing
			20.4.1 True Color Enhancement
			20.4.2 Saturation and Hue Enhancement
			20.4.3 False Color Enhancement
			20.4.4 Color Image Processing
			20.4.5 Color Ordering and Edges
			20.4.6 Color Image Histogram
	Chapter 21: Video Image Processing
		21.1 Video
			21.1.1 Analog and Digital Video
			21.1.2 Various Video
			21.1.3 Video Frame
			21.1.4 Video Scan and Display
			21.1.5 Video Display
		21.2 Video Terminology
			21.2.1 Video Terms
			21.2.2 Video Processing and Techniques
		21.3 Video Enhancement
			21.3.1 Video Enhancement
			21.3.2 Motion-Based Filtering
			21.3.3 Block Matching
		21.4 Video Coding
			21.4.1 Video Codec
			21.4.2 Intra-frame Coding
			21.4.3 Inter-frame Coding
		21.5 Video Computation
			21.5.1 Image Sequence
			21.5.2 Video Analysis
	Chapter 22: Multi-resolution Image
		22.1 Multi-resolution and Super-Resolution
			22.1.1 Multi-resolution
			22.1.2 Super-Resolution
		22.2 Multi-scale Images
			22.2.1 Multi-scales
			22.2.2 Multi-scale Space
			22.2.3 Multi-scale Transform
		22.3 Image Pyramid
			22.3.1 Pyramid Structure
			22.3.2 Gaussian and Laplacian Pyramids
Part III: Image Analysis
	Chapter 23: Segmentation Introduction
		23.1 Segmentation Overview
			23.1.1 Segmentation Definition
			23.1.2 Object and Background
			23.1.3 Method Classification
			23.1.4 Various Strategies
		23.2 Primitive Unit Detection
			23.2.1 Point Detection
			23.2.2 Corner Detection
			23.2.3 Line Detection
			23.2.4 Curve Detection
		23.3 Geometric Unit Detection
			23.3.1 Bar Detection
			23.3.2 Circle and Ellipse Detection
			23.3.3 Object Contour
			23.3.4 Hough Transform
		23.4 Image Matting
			23.4.1 Matting Basics
			23.4.2 Matting Techniques
	Chapter 24: Edge Detection
		24.1 Principle
			24.1.1 Edge Detection
			24.1.2 Sub-pixel Edge
		24.2 Various Edges
			24.2.1 Type of Edge
			24.2.2 Edge Description
		24.3 Gradients and Gradient Operators
			24.3.1 Gradient Computation
			24.3.2 Differential Edge Detector
			24.3.3 Gradient Operators
			24.3.4 Particle Gradient Operators
			24.3.5 Orientation Detection
		24.4 High-Order Detectors
			24.4.1 Second-Derivative Detectors
			24.4.2 Gaussian-Laplacian Detectors
			24.4.3 Other Detectors
	Chapter 25: Object Segmentation Methods
		25.1 Parallel-Boundary Techniques
			25.1.1 Boundary Segmentation
			25.1.2 Boundary Points
			25.1.3 Boundary Thinning Techniques
		25.2 Sequential-Boundary Techniques
			25.2.1 Basic Techniques
			25.2.2 Graph Search
			25.2.3 Active Contour
			25.2.4 Snake
			25.2.5 General Active Contour
			25.2.6 Graph Cut
		25.3 Parallel-Region Techniques
			25.3.1 Thresholding
			25.3.2 Global Thresholding Techniques
			25.3.3 Local Thresholding Techniques
			25.3.4 Clustering and Mean Shift
		25.4 Sequential-Region Techniques
			25.4.1 Region Growing
			25.4.2 Watershed
			25.4.3 Level Set
		25.5 More Segmentation Techniques
	Chapter 26: Segmentation Evaluation
		26.1 Evaluation Scheme and Framework
		26.2 Evaluation Methods and Criteria
			26.2.1 Analytical Methods and Criteria
			26.2.2 Empirical Goodness Methods and Criteria
			26.2.3 Empirical Discrepancy Methods and Criteria
			26.2.4 Empirical Discrepancy of Pixel Numbers
		26.3 Systematic Comparison and Characterization
	Chapter 27: Object Representation
		27.1 Object Representation Methods
			27.1.1 Object Representation
			27.1.2 Spline {8}
		27.2 Boundary-Based Representation
			27.2.1 Boundary Representation
			27.2.2 Boundary Signature
			27.2.3 Curve Representation
			27.2.4 Parametric Curve
			27.2.5 Curve Fitting
			27.2.6 Chain Codes
		27.3 Region-Based Representation
			27.3.1 Polygon
			27.3.2 Surrounding Region
			27.3.3 Medial Axis Transform
			27.3.4 Skeleton
			27.3.5 Region Decomposition
	Chapter 28: Object Description
		28.1 Object Description Methods
			28.1.1 Object Description
			28.1.2 Feature Description
		28.2 Boundary-Based Description
			28.2.1 Boundary
			28.2.2 Curvature
		28.3 Region-Based Description
			28.3.1 Region Description
			28.3.2 Moment Description
		28.4 Descriptions of Object Relationship
			28.4.1 Object Relationship
			28.4.2 Image Topology
		28.5 Attributes
		28.6 Object Saliency
	Chapter 29: Feature Measurement and Error Analysis
		29.1 Feature Measurement
			29.1.1 Metric
			29.1.2 Object Measurement
			29.1.3 Local Invariance
			29.1.4 More Invariance
		29.2 Accuracy and Precision
		29.3 Error Analysis
			29.3.1 Measurement Error
			29.3.2 Residual and Error
	Chapter 30: Texture Analysis
		30.1 Texture Overview
			30.1.1 Texture
			30.1.2 Texture Elements
			30.1.3 Texture Analysis
			30.1.4 Texture Models
		30.2 Texture Feature and Description
			30.2.1 Texture Features
			30.2.2 Texture Description
		30.3 Statistical Approach
			30.3.1 Texture Statistics
			30.3.2 Co-occurrence Matrix
		30.4 Structural Approach
			30.4.1 Structural Texture
			30.4.2 Local Binary Pattern
		30.5 Spectrum Approach
		30.6 Texture Segmentation
		30.7 Texture Composition
			30.7.1 Texture Categorization
			30.7.2 Texture Generation
	Chapter 31: Shape Analysis
		31.1 Shape Overview
			31.1.1 Shape
			31.1.2 Shape Analysis
		31.2 Shape Representation and Description
			31.2.1 Shape Representation
			31.2.2 Shape Model
			31.2.3 Shape Description
			31.2.4 Shape Descriptors
		31.3 Shape Classification
		31.4 Shape Compactness
			31.4.1 Compactness and Elongation
			31.4.2 Specific Descriptors
		31.5 Shape Complexity
		31.6 Delaunay and Voronoï Meshes
			31.6.1 Mesh Model
			31.6.2 Delaunay Meshes
			31.6.3 Voronoï Meshes
			31.6.4 Maximal Nucleus Cluster
	Chapter 32: Motion Analysis
		32.1 Motion and Analysis
			32.1.1 Motion
			32.1.2 Motion Classification
			32.1.3 Motion Estimation
			32.1.4 Various Motion Estimations
			32.1.5 Motion Analysis and Understanding
		32.2 Motion Detection and Representation
			32.2.1 Motion Detection
			32.2.2 Motion Representation
		32.3 Moving Object Detection
			32.3.1 Object Detection
			32.3.2 Object Trajectory
		32.4 Moving Object Tracking
			32.4.1 Feature Tracking
			32.4.2 Object Tracking
			32.4.3 Object Tracking Techniques
			32.4.4 Kalman Filter
			32.4.5 Particle Filtering
		32.5 Motion and Optical Flows
			32.5.1 Motion Field
			32.5.2 Optical Flow
			32.5.3 Optical Flow Field
			32.5.4 Optical Flow Equation
	Chapter 33: Image Pattern Recognition
		33.1 Pattern
		33.2 Pattern Recognition
			33.2.1 Recognition
			33.2.2 Recognition Categories
			33.2.3 Image Recognition
			33.2.4 Various Recognition Methods
		33.3 Pattern Classification
			33.3.1 Category
			33.3.2 Classification
			33.3.3 Test and Verification
		33.4 Feature and Detection
			33.4.1 Feature
			33.4.2 Feature Analysis
		33.5 Feature Dimension Reduction
			33.5.1 Dimension Reduction
			33.5.2 Manifold and Independent Component
		33.6 Classifier and Perceptron
			33.6.1 Classifier
			33.6.2 Optimal Classifier
			33.6.3 Support Vector Machine
			33.6.4 Perceptron
		33.7 Clustering
			33.7.1 Cluster
			33.7.2 Cluster Analysis
		33.8 Discriminant and Decision Function
			33.8.1 Discriminant Function
			33.8.2 Kernel Discriminant
			33.8.3 Decision Function
		33.9 Syntactic Recognition
			33.9.1 Grammar and Syntactic
			33.9.2 Automaton
		33.10 Test and Error
			33.10.1 Test
			33.10.2 True
			33.10.3 Error
	Chapter 34: Biometric Recognition
		34.1 Human Biometrics
		34.2 Subspace Techniques
		34.3 Face Recognition and Analysis
			34.3.1 Face Detection
			34.3.2 Face Tracking
			34.3.3 Face Recognition
			34.3.4 Face Image Analysis
		34.4 Expression Analysis
			34.4.1 Facial Expression
			34.4.2 Facial Expression Analysis
		34.5 Human Body Recognition
			34.5.1 Human Motion
			34.5.2 Other Analysis
		34.6 Other Biometrics
			34.6.1 Fingerprint and Gesture
			34.6.2 More Biometrics
Part IV: Image Understanding
	Chapter 35: Theory of Image Understanding
		35.1 Understanding Models
			35.1.1 Computational Structures
			35.1.2 Active, Qualitative, and Purposive Vision
		35.2 Marr´s Visual Computational Theory
			35.2.1 Theory Framework
			35.2.2 Three-Layer Representations
	Chapter 36: 3-D Representation and Description
		36.1 3-D Point and Curve
			36.1.1 3-D Point
			36.1.2 Curve and Conic
			36.1.3 3-D Curve
		36.2 3-D Surface Representation
			36.2.1 Surface
			36.2.2 Surface Model
			36.2.3 Surface Representation
			36.2.4 Surface Description
			36.2.5 Surface Classification
			36.2.6 Curvature and Classification
			36.2.7 Various Surfaces
		36.3 3-D Surface Construction
			36.3.1 Surface Construction
			36.3.2 Construction Techniques
		36.4 Volumetric Representation
			36.4.1 Volumetric Models
			36.4.2 Volumetric Representation Methods
			36.4.3 Generalized Cylinder Representation
	Chapter 37: Stereo Vision
		37.1 Stereo Vision Overview
			37.1.1 Stereo
			37.1.2 Stereo Vision
			37.1.3 Disparity
			37.1.4 Constraint
			37.1.5 Epipolar
			37.1.6 Rectification
		37.2 Binocular Stereo Vision
			37.2.1 Binocular Vision
			37.2.2 Correspondence
			37.2.3 SIFT and SURF
		37.3 Multiple-Ocular Stereo Vision
			37.3.1 Multibaselines
			37.3.2 Trinocular
			37.3.3 Multiple-Nocular
			37.3.4 Post-processing
	Chapter 38: Multi-image 3-D Scene Reconstruction
		38.1 Scene Recovery
			38.1.1 3-D Reconstruction
			38.1.2 Depth Estimation
			38.1.3 Occlusion
		38.2 Photometric Stereo Analysis
			38.2.1 Photometric Stereo
			38.2.2 Illumination Models
		38.3 Shape from X
			38.3.1 Various reconstructions
			38.3.2 Structure from Motion
			38.3.3 Shape from Optical Flow
	Chapter 39: Single-Image 3-D Scene Reconstruction
		39.1 Single-Image Reconstruction
		39.2 Various Reconstruction Cues
			39.2.1 Focus
			39.2.2 Texture
			39.2.3 Shading
			39.2.4 Shadow
			39.2.5 Other Cues
	Chapter 40: Knowledge and Learning
		40.1 Knowledge and Model
			40.1.1 Knowledge Classification
			40.1.2 Procedure Knowledge
			40.1.3 Models
			40.1.4 Model Functions
		40.2 Knowledge Representation Schemes
			40.2.1 Knowledge Representation Models
			40.2.2 Knowledge Base
			40.2.3 Logic System
		40.3 Learning
			40.3.1 Statistical Learning
			40.3.2 Machine Learning
			40.3.3 Zero-Shot and Ensemble Learning
			40.3.4 Various Learning Methods
		40.4 Inference
			40.4.1 Inference Classification
			40.4.2 Propagation
	Chapter 41: General Image Matching
		41.1 General Matching
			41.1.1 Matching
			41.1.2 Matching Function
			41.1.3 Matching Techniques
		41.2 Image Matching
			41.2.1 Image Matching Techniques
			41.2.2 Feature Matching Techniques
			41.2.3 Correlation and Cross-Correlation
			41.2.4 Mask Matching Techniques
			41.2.5 Diverse Matching Techniques
		41.3 Image Registration
			41.3.1 Registration
			41.3.2 Image Registration Methods
			41.3.3 Image Alignment
			41.3.4 Image Warping
		41.4 Graph Isomorphism and Line Drawing
			41.4.1 Graph Matching
			41.4.2 Line Drawing
			41.4.3 Contour Labeling
	Chapter 42: Scene Analysis and Interpretation
		42.1 Scene Interpretation
			42.1.1 Image Scene
			42.1.2 Scene Analysis
			42.1.3 Scene Understanding
			42.1.4 Scene Knowledge
		42.2 Interpretation Techniques
			42.2.1 Soft Computing
			42.2.2 Labeling
			42.2.3 Fuzzy Set
			42.2.4 Fuzzy Calculation
			42.2.5 Classification Models
	Chapter 43: Image Information Fusion
		43.1 Information Fusion
			43.1.1 Multi-sensor Fusion
			43.1.2 Mosaic Fusion Techniques
		43.2 Evaluation of Fusion Result
		43.3 Layered Fusion Techniques
			43.3.1 Three Layers
			43.3.2 Method for Pixel Layer Fusion
			43.3.3 Method for Feature Layer Fusion
			43.3.4 Method for Decision Layer Fusion
	Chapter 44: Content-Based Retrieval
		44.1 Visual Information Retrieval
			44.1.1 Information Content Retrieval
			44.1.2 Image Retrieval
			44.1.3 Image Querying
			44.1.4 Database Indexing
			44.1.5 Image Indexing
		44.2 Feature-Based Retrieval
			44.2.1 Features and Retrieval
			44.2.2 Color-Based Retrieval
		44.3 Video Organization and Retrieval
			44.3.1 Video Organization
			44.3.2 Abrupt and Gradual Changes
			44.3.3 Video Structuring
			44.3.4 News Program Organization
		44.4 Semantic Retrieval
			44.4.1 Semantic-Based Retrieval
			44.4.2 Multilayer Image Description
			44.4.3 Higher Level Semantics
			44.4.4 Video Understanding
	Chapter 45: Spatial-Temporal Behavior Understanding
		45.1 Spatial-Temporal Techniques
			45.1.1 Techniques and Layers
			45.1.2 Spatio-Temporal Analysis
			45.1.3 Action Behavior Understanding
		45.2 Action and Pose
			45.2.1 Action Models
			45.2.2 Action Recognition
			45.2.3 Pose Estimation
			45.2.4 Posture Analysis
		45.3 Activity and Analysis
			45.3.1 Activity
			45.3.2 Activity Analysis
		45.4 Events
			45.4.1 Event Detection
			45.4.2 Event Understanding
		45.5 Behavior and Understanding
			45.5.1 Behavior
			45.5.2 Behavior Analysis
			45.5.3 Behavior Interpretation
			45.5.4 Petri Net
Part V: Related References
	Chapter 46: Related Theories and Techniques
		46.1 Random Field
			46.1.1 Random Variables
			46.1.2 Random Process
			46.1.3 Random Fields
			46.1.4 Markov Random Field
			46.1.5 Markov Models
			46.1.6 Markov Process
		46.2 Bayesian Statistics
			46.2.1 Bayesian Model
			46.2.2 Bayesian Laws and Rules
			46.2.3 Belief Networks
		46.3 Graph Theory
			46.3.1 Tree
			46.3.2 Graph
			46.3.3 Graph Representation
			46.3.4 Graph Geometric Representation
			46.3.5 Directed Graph
			46.3.6 Graph Model
			46.3.7 Graph Classification
		46.4 Compressive Sensing
			46.4.1 Introduction
			46.4.2 Sparse Representation
			46.4.3 Measurement Coding and Decoding Reconstruction
		46.5 Neural Networks
			46.5.1 Neural Networks
			46.5.2 Special Neural Networks
			46.5.3 Training and Fitting
			46.5.4 Network Operations
			46.5.5 Activation Functions
		46.6 Various Theories and Techniques
			46.6.1 Optimalization
			46.6.2 Kernels
			46.6.3 Stereology
			46.6.4 Relaxation and Expectation Maximization
			46.6.5 Context and RANSAC
			46.6.6 Miscellaneous
	Chapter 47: Optics
		47.1 Optics and Instruments
			47.1.1 Classifications
			47.1.2 Instruments
		47.2 Photometry
			47.2.1 Intensity
			47.2.2 Emission and Transmission
			47.2.3 Optical Properties of the Surface
		47.3 Ray Radiation
			47.3.1 Radiation
			47.3.2 Radiometry
			47.3.3 Radiometry Standards
			47.3.4 Special Lights
		47.4 Spectroscopy
			47.4.1 Spectrum
			47.4.2 Spectroscopy
			47.4.3 Spectral Analysis
			47.4.4 Interaction of Light and Matter
		47.5 Geometric Optics
			47.5.1 Ray
			47.5.2 Reflection
			47.5.3 Various Reflections
			47.5.4 Refraction
		47.6 Wave Optics
			47.6.1 Light Wave
			47.6.2 Scattering and Diffraction
	Chapter 48: Mathematical Morphology for Binary Images
		48.1 Image Morphology
			48.1.1 Morphology Fundamentals
			48.1.2 Morphological Operations
			48.1.3 Morphological Image Processing
		48.2 Binary Morphology
			48.2.1 Basic Operations
			48.2.2 Combined Operations and Practical Algorithms
	Chapter 49: Mathematical Morphology for Gray-Level Images
		49.1 Gray-Level Morphology
			49.1.1 Ordering Relations
			49.1.2 Basic Operations
			49.1.3 Combined Operations and Practical Algorithms
		49.2 Soft Morphology
	Chapter 50: Visual Sensation and Perception
		50.1 Human Visual System
			50.1.1 Human Vision
			50.1.2 Organ of Vision
			50.1.3 Visual Process
		50.2 Eye Structure and Function
			50.2.1 Eye Structure
			50.2.2 Retina
			50.2.3 Photoreceptor
		50.3 Visual Sensation
			50.3.1 Sensation
			50.3.2 Brightness
			50.3.3 Photopic and Scotopic Vision
			50.3.4 Subjective Brightness
			50.3.5 Vision Characteristics
			50.3.6 Virtual Vision
		50.4 Visual Perception
			50.4.1 Perceptions
			50.4.2 Perceptual Constancy
			50.4.3 Theory of Color Vision
			50.4.4 Color Vision Effect
			50.4.5 Color Science
			50.4.6 Visual Attention
		50.5 Visual Psychology
			50.5.1 Laws of Visual Psychology
			50.5.2 Illusion
			50.5.3 Illusion of Geometric Figure and Reason Theory
	Chapter 51: Application of Image Technology
		51.1 Television
			51.1.1 Digital Television
			51.1.2 Color Television
		51.2 Visual Surveillance
			51.2.1 Surveillance
			51.2.2 Visual Inspection
			51.2.3 Visual Navigation
			51.2.4 Traffic
		51.3 Other Applications
			51.3.1 Document and OCR
			51.3.2 Medical Images
			51.3.3 Remote Sensing
			51.3.4 Various Applications
	Chapter 52: International Organizations and Standards
		52.1 Organizations
			52.1.1 International Organizations
			52.1.2 National Organizations
		52.2 Image and Video Coding Standards
			52.2.1 Binary Image Coding Standards
			52.2.2 Grayscale Image Coding Standards
			52.2.3 Video Coding Standards: MPEG
			52.2.4 Video Coding Standards: H.26x
			52.2.5 Other Standards
		52.3 Public Systems and Databases
			52.3.1 Public Systems
			52.3.2 Public Databases
			52.3.3 Face Databases
		52.4 Other Standards
			52.4.1 International System of Units
			52.4.2 CIE Standards
			52.4.3 MPEG Standards
			52.4.4 Various Standards
Bibliography
Index