Across three volumes, the Handbook of Image Processing and Computer Vision presents a comprehensive review of the full range of topics that comprise the field of computer vision, from the acquisition of signals and formation of images, to learning techniques for scene understanding. The authoritative insights presented within cover all aspects of the sensory subsystem required by an intelligent system to perceive the environment and act autonomously. Volume 1 (From Energy to Image) examines the formation, properties, and enhancement of a digital image.
Topics and features:
• Describes the fundamental processes in the field of artificial vision that enable the formation of digital images from light energy
• Covers light propagation, color perception, optical systems, and the analog-to-digital conversion of the signal
• Discusses the information recorded in a digital image, and the image processing algorithms that can improve the visual qualities of the image
• Reviews boundary extraction algorithms, key linear and geometric transformations, and techniques for image restoration
• Presents a selection of different image segmentation algorithms, and of widely-used algorithms for the automatic detection of points of interest
• Examines important algorithms for object recognition, texture analysis, 3D reconstruction, motion analysis, and camera calibration
• Provides an introduction to four significant types of neural network, namely RBF, SOM, Hopfield, and deep neural networks
This all-encompassing survey offers a complete reference for all students, researchers, and practitioners involved in developing intelligent machine vision systems. The work is also an invaluable resource for professionals within the IT/software and electronics industries involved in machine vision, imaging, and artificial intelligence.
Dr. Cosimo Distante is a Research Scientist in Computer Vision and Pattern Recognition in the Institute of Applied Sciences and Intelligent Systems (ISAI) at the Italian National Research Council (CNR). Dr. Arcangelo Distante is a researcher and the former Director of the Institute of Intelligent Systems for Automation (ISSIA) at the CNR. His research interests are in the fields of Computer Vision, Pattern Recognition, Machine Learning, and Neural Computation.
Author(s): Arcangelo Distante; Cosimo Distante
Publisher: Springer Nature
Year: 2020
Preface
Acknowledgments
Contents
1 Image Formation Process
1.1 Introduction
1.2 From Energy to Image
1.3 Electromagnetic Energy, Photons and Light
1.3.1 Characteristic of Electromagnetic Waves
1.4 The Energy of Electromagnetic Waves
1.5 Sources of Electromagnetic Waves
1.6 Light–Matter Interaction
1.7 Photons
1.8 Propagation of Electromagnetic Waves in Matter
1.9 The Spectrum of Electromagnetic Radiation
1.10 The Light
1.10.1 Propagation of Light
1.10.2 Reflection and Refraction
1.11 The Physics of Light
1.12 Energy of an Electromagnetic Wave
1.13 Reflectance and Transmittance
1.13.1 Angle of Brewster
1.13.2 Internal Reflection
1.14 Thermal Radiation
1.15 Photometric Magnitudes
1.16 Functions of Visual Luminosity
2 Radiometric Model
2.1 Introduction
2.2 Light Sources and Radiometric Aspects
2.3 Bidirectional Reflectance Distribution Function—BRDF
2.3.1 Lambertian Model
2.3.2 Model of Specular Reflectance
2.3.3 Lambertian–Specular Compound Reflectance Model
2.3.4 Phong Model
2.4 Fundamental Equation in the Process of Image Formation
3 Color
3.1 Introduction
3.1.1 The Theory of Color Perception
3.2 The Human Visual System
3.3 Visual Phenomena: Sensitivity to Contrast
3.4 Visual Phenomena: Simultaneous Contrast
3.5 Visual Phenomena: Bands of Mach
3.6 Visual Phenomena: Color Blindness
3.7 The Colors of Nature
3.8 Constancy of Color
3.9 Colorimetry
3.9.1 Metamerism and Grassmann's Law
3.10 Additive Synthesis Method
3.10.1 Tristimulus Curves of Equal Radiance
3.10.2 Chromaticity Coordinates
3.11 3D Representation of RGB Color
3.12 XYZ Color Coordinates
3.13 Chromaticity Diagram—RGB
3.14 Chromaticity Diagram—XYZ
3.14.1 Calculation of the Positions of the RGB Primaries in the Chromaticity Diagram Xy
3.14.2 Analysis of the Transformation from RGB to the XYZ System
3.15 Geometric Representation of Color
3.16 HSI Color Space
3.17 The Color in Image Processing
3.18 RGB to the HSI Space Conversion
3.18.1 RGB rightarrow HSI
3.18.2 HSI rightarrow RGB
3.19 HSV and HSL Color Space
3.20 CIE 1960/64 UCS Color Space
3.21 CIE 1976 L*a*b* Color Space
3.22 CIE 1976 L*u*v* Color Space
3.23 CIELab LCh and CIELuv LCh Color Spaces
3.24 YIQ Color Space
3.25 Subtractive Synthesis Method
3.26 Color Reproduction Technologies
3.27 Summary and Conclusions
4 Optical System
4.1 Introduction
4.2 Reflection of Light on Spherical Mirrors
4.3 Refraction of Light on Spherical Surfaces
4.4 Thin Lens
4.4.1 Diagram of the Main Rays for Thin Lenses
4.4.2 Optical Magnification: Microscope and Telescope
4.5 Optical Aberrations
4.5.1 Parameters of an Optical System
5 Digitization and Image Display
5.1 Introduction
5.2 The Human Optical System
5.3 Image Acquisition Systems
5.4 Representation of the Digital Image
5.5 Resolution and Spatial Frequency
5.6 Geometric Model of Image Formation
5.7 Image Formation with a Real Optical System
5.8 Resolution of the Optical System
5.8.1 Contrast Modulation Function—MTF
5.8.2 Optical Transfer Function (OTF)
5.9 Sampling
5.10 Quantization
5.11 Digital Image Acquisition Systems—DIAS
5.11.1 Field of View—FoV
5.11.2 Focal Length of the Optical System
5.11.3 Spatial Resolution of Optics
5.11.4 Spatial Size and Resolution of the Sensor
5.11.5 Time Resolution of the Sensor
5.11.6 Depth of Field and Focus
5.11.7 Depth of Field Calculation
5.11.8 Calculation of Hyperfocal
5.11.9 Depth of Focus
5.11.10 Camera
5.11.11 Video Camera
5.11.12 Infrared Camera
5.11.13 Time-of-Flight Camera—ToF
5.12 Microscopy
5.13 Telescopic
5.14 The MTF Function of an Image Acquisition System
6 Properties of the Digital Image
6.1 Digital Binary Image
6.2 Pixel Neighborhood
6.3 Image Metric
6.3.1 Euclidean Distance
6.3.2 City Block Distance
6.3.3 Chessboard Distance
6.4 Distance Transform
6.5 Path
6.6 Adjacency and Connectivity
6.7 Region
6.7.1 Connected Component
6.7.2 Foreground Background and Holes
6.7.3 Object
6.7.4 Contour
6.7.5 Edges
6.8 Topological Properties of the Image
6.8.1 Euler Number
6.8.2 Convex Hull
6.8.3 Area, Perimeter and Compactness
6.9 Property Independent of Pixel Position
6.9.1 Histogram
6.10 Correlation-Dependent Property Between Pixels
6.10.1 The Image as a Stochastic Process ! Random Field
6.10.2 Correlation Measurement
6.11 Image Quality
6.11.1 Image Noise
6.11.2 Gaussian Noise
6.11.3 Salt-and-Pepper Noise
6.11.4 Impulsive Noise
6.11.5 Noise Management
6.12 Perceptual Information of the Image
6.12.1 Contrast
6.12.2 Acuteness
7 Data Organization
7.1 Data in the Different Levels of Processing
7.2 Data Structures
7.2.1 Matrix
7.2.2 Co-Occurrence Matrix
7.3 Contour Encoding (Chain Code)
7.4 Run-Length Encoding
7.4.1 Run-Length Code for Grayscale and Color Images
7.5 Topological Organization of Data-Graph
7.5.1 Region Adjacency Graph (RAG)
7.5.2 Features of RAG
7.5.3 Algorithm to Build RAG
7.5.4 Relational Organization
7.6 Hierarchical Structure of Data
7.6.1 Pyramids
7.6.2 Quadtree
7.6.3 T-Pyramid
7.6.4 Gaussian and Laplacian Pyramid
7.6.5 Octree
7.6.6 Operations on Quadtree and Octree
8 Representation and Description of Forms
8.1 Introduction
8.2 External Representation of Objects
8.2.1 Chain Code
8.2.2 Polygonal Approximation—Perimeter
8.2.3 Polygonal Approximation—Splitting
8.2.4 Polygonal Approximation—Merging
8.2.5 Contour Approximation with Curved Segments
8.2.6 Signature
8.2.7 Representation by Convex Hull
8.2.8 Representation by Means of Skeletonization
8.3 Description of the Forms
8.3.1 Shape Elementary Descriptors
8.3.2 Statistical Moments
8.3.3 Moments Based on Orthogonal Basis Functions
8.3.4 Fourier Descriptors
9 Image Enhancement Techniques
9.1 Introduction to Computational Levels
9.2 Improvement of Image Quality
9.2.1 Image Histogram
9.2.2 Probability Density Function and Cumulative Distribution Function of Image
9.2.3 Contrast Manipulation
9.2.4 Gamma Transformation
9.3 Histogram Modification
9.3.1 Histogram Equalization
9.3.2 Adaptive Histogram Equalization (AHE)
9.3.3 Contrast Limited Adaptive Histogram Equalization (CLAHE)
9.4 Histogram Specification
9.5 Homogeneous Point Operations
9.6 Nonhomogeneous Point Operations
9.6.1 Point Operator to Correct the Radiometric Error
9.6.2 Local Statistical Operator
9.7 Color Image Enhancement
9.7.1 Natural Color Images
9.7.2 Pseudo-color Images
9.7.3 False Color Images
9.8 Improved Quality of Multispectral Images
9.9 Towards Local and Global Operators
9.9.1 Numerical Spatial Filtering
9.10 Spatial Convolution
9.10.1 1D Spatial Convolution
9.10.2 2D Spatial Convolution
9.11 Filtering in the Frequency Domain
9.11.1 Discrete Fourier Transform DFT
9.11.2 Frequency Response of Linear System
9.11.3 Convolution Theorem
9.12 Local Operators: Smoothing
9.12.1 Arithmetic Average
9.12.2 Average Filter
9.12.3 Nonlinear Filters
9.12.4 Median Filter
9.12.5 Minimum and Maximum Filter
9.12.6 Gaussian Smoothing Filter
9.12.7 Binomial Filters
9.12.8 Computational Analysis of Smoothing Filters
9.13 Low Pass Filtering in the Fourier Domain
9.13.1 Ideal Low Pass Filter
9.13.2 Butterworth Low Pass Filter
9.13.3 Gaussian Low Pass Filter
9.13.4 Trapezoidal Low Pass Filter
9.13.5 Summary of the Results of the Smoothing Filters
Index
