Machine Vision: Automated Visual Inspection: Theory, Practice and Applications

Preface
Contents
Chapter 1 Introduction
	1 Introduction
		1.1 Visual inspection
		1.2 Optical capturing of test objects
		1.3 Formation and definition of an image
		1.4 Machine vision
		1.5 Practical approach for performing machine vision projects
		1.6 Bibliography
Part I Image Acquisition
	Chapter 2 Light
		2 Light
			2.1 The phenomenon of light
				2.1.1 The electromagnetic spectrum
			2.2 Light as an electromagnetic wave
				2.2.1 Maxwell’s equations
				2.2.2 Polarization
				2.2.3 Huygens’ principle
				2.2.4 Coherence
				2.2.5 Interference
				2.2.6 Diffraction
				2.2.7 Speckle
			2.3 Light as a quantum phenomenon
			2.4 The ray model of geometrical optics
			2.5 Summary
			2.6 Interaction of light and matter
				2.6.1 Absorption
				2.6.2 The law of reflection
				2.6.3 The law of refraction
				2.6.4 Scattering
				2.6.5 The Fresnel coefficients for reflection and transmission
				2.6.6 Electromagnetic waves in conductive media
			2.7 Light sources
				2.7.1 Thermal radiators
				2.7.2 Gas-discharge lamps
				2.7.3 Light-emitting diodes
				2.7.4 Laser
				2.7.5 Summary
			2.8 Bibliography
	Chapter 3 Optical Imaging
		3 Optical Imaging
			3.1 Introduction
			3.2 Imaging with a pinhole camera, central projection
			3.3 The camera model and camera calibration
			3.4 Optical imaging using a single lens
				3.4.1 The paraxial approximation and Gaussian optics
				3.4.2 Thin lens equation
				3.4.3 Bundle limitation
				3.4.4 Depth of field
				3.4.5 Telecentric imaging
				3.4.6 Perspective
				3.4.7 Imaging of tilted planes
				3.4.8 Aberrations
			3.5 Optical instruments with several lenses
				3.5.1 The projector
				3.5.2 The microscope
			3.6 Bibliography
	Chapter 4 Radiometry
		4 Radiometry
			4.1 Radiometric quantities
			4.2 The light field of a test object
			4.3 The bidirectional reflectance distribution function (BRDF)
				4.3.1 BRDF and scattered light
			4.4 Formation of image values
				4.4.1 Application to a thin lens
			4.5 Bibliography
	Chapter 5 Color
		5 Color
			5.1 Photometry
			5.2 Color perception and color spaces
				5.2.1 Color perception of the human eye
				5.2.2 Color mixing
				5.2.3 CIE color spaces
				5.2.4 Spectrophotometry for color measurement and color distance computation
				5.2.5 Color order systems
				5.2.6 Other color spaces
			5.3 Filters
			5.4 Acquisition and processing of color images
			5.5 Bibliography
	Chapter 6 Sensors for Image Acquisition
		6 Sensors for Image Acquisition
			6.1 Point, line and area sensors
			6.2 Image tube cameras
			6.3 Photomultipliers
				6.3.1 Image intensifiers
			6.4 Photodiodes
			6.5 Position sensitive detectors (PSD)
			6.6 Charge-coupled device (CCD)
			6.7 Complementary metal-oxide-semiconductor (CMOS) sensors
			6.8 Line-scan cameras
			6.9 Color sensors and color cameras
			6.10 Infrared cameras
				6.10.1 Bolometer cameras
				6.10.2 Infrared quantum detector cameras
			6.11 Quality criteria for image sensors
			6.12 Bibliography
	Chapter 7 Methods of Image Acquisition
		7 Methods of Image Acquisition
			7.1 Introduction
			7.2 Measuring optical properties
				7.2.1 Measurement of the complex index of refraction
				7.2.2 Fluorescence
				7.2.3 Methods for measuring the reflectance
				7.2.4 Spectral sensors
				7.2.5 Light scattering methods and the inspection of surface roughness
			7.3 3D shape capturing
				7.3.1 Triangulation (point-by-point scanning)
				7.3.2 Light-section methods (line scanning)
				7.3.3 The measurement uncertainty of triangulation
				7.3.4 Structured illumination
				7.3.5 Deflectometry
				7.3.6 The moiré method
				7.3.7 Final remark on structured illumination
				7.3.8 Stereo images
				7.3.9 Light-field cameras
				7.3.10 Silhouette capturing
				7.3.11 Shape from shading
				7.3.12 Autofocus sensors
				7.3.13 Confocal microscopy
				7.3.14 Confocal chromatic triangulation
				7.3.15 Time-of-flight sensors
				7.3.16 Phase-based methods
			7.4 Capturing interior object structures
				7.4.1 Thermography
				7.4.2 Imaging using X-rays
				7.4.3 Optical coherence tomography
				7.4.4 Schlieren imaging and schlieren tomography
				7.4.5 Image acquisition using terahertz radiation
				7.4.6 Photoelasticity
			7.5 Special image acquisition methods
				7.5.1 Image acquisition systems with variable illumination direction
				7.5.2 Endoscopy
			7.6 Universal principles
				7.6.1 Suppression of extraneous light
				7.6.2 Inverse illumination
			7.7 Summary
			7.8 Bibliography
Part II Image Processing
	Chapter 8 Image Signals
		8 Image Signals
			8.1 Mathematical model of image signals
			8.2 Systems and signals
				8.2.1 System characteristics
				8.2.2 The Dirac delta function
				8.2.3 Convolution
			8.3 The Fourier transform
				8.3.1 The one-dimensional Fourier transform
				8.3.2 The one-dimensional sampling theorem
				8.3.3 The discrete Fourier transform (DFT)
				8.3.4 The two-dimensional Fourier transform
				8.3.5 Dirac delta functions in two-dimensional space
				8.3.6 The two-dimensional Heaviside function
				8.3.7 Sampling of two-dimensional signals
				8.3.8 Sampling theorem for two-dimensional signals
				8.3.9 The two-dimensional DFT
			8.4 Examples of use concerning system theory and the Fourier transform
			8.5 Image signals as stochastic processes
				8.5.1 Moments of stochastic processes
				8.5.2 Stationarity and ergodicity
				8.5.3 Passing a stochastic process through an LSI system
			8.6 Quantization
				8.6.1 Optimal quantization
				8.6.2 The quantization theorem
				8.6.3 Modeling of the quantization
			8.7 The Karhunen–Loève transform
				8.7.1 Definition of the Karhunen–Loève transform
				8.7.2 Properties of the Karhunen–Loève transform
				8.7.3 Examples of application of the Karhunen–Loève transform
			8.8 Bibliography
	Chapter 9 Preprocessing and Image Enhancement
		9 Preprocessing and Image Enhancement
			9.1 Simple image enhancement methods
				9.1.1 Contrast adjustment by histogram stretching
				9.1.2 Histogram manipulation
				9.1.3 Pseudo-color and false-color images
				9.1.4 Image sharpening
			9.2 Reduction of systematic errors
				9.2.1 Geometric rectification
				9.2.2 Suppression of inhomogeneities
			9.3 Attenuation of random disturbances
				9.3.1 Linear filters
				9.3.2 Noise reduction using nonlinear filters
			9.4 Image registration
			9.5 Bibliography
	Chapter 10 Image Restoration
		10 Image Restoration
			10.1 Signal model
			10.2 Inverse filter
			10.3 The Wiener filter
			10.4 The geometric mean filter
			10.5 Optimal constraint filter
			10.6 Restoration problems in matrix notation
			10.7 Restoration for participating media
			10.8 Spatially-varying image restoration
			10.9 Bibliography
	Chapter 11 Segmentation
		11 Segmentation
			11.1 Region-based segmentation
				11.1.1 Segmentation by feature-based classification
				11.1.2 Region growing methods
			11.2 Edge-oriented methods
				11.2.1 Gradient filters
				11.2.2 Edge detection using the second derivative
				11.2.3 The watershed transformation
			11.3 Diffusion filters
				11.3.1 Linear, homogeneous, isotropic image diffusion
				11.3.2 Linear, inhomogeneous, isotropic image diffusion
				11.3.3 Nonlinear, inhomogeneous, isotropic image diffusion
				11.3.4 Nonlinear, inhomogeneous, anisotropic image diffusion
			11.4 Active contours
				11.4.1 Gradient vector flow
				11.4.2 Vector field convolution
			11.5 Segmentation according to Mumford and Shah
			11.6 Segmentation using graph cut methods
			11.7 Bibliography
	Chapter 12 Morphological Image Processing
		12 Morphological Image Processing
			12.1 Binary morphology
				12.1.1 Point sets and structuring elements
				12.1.2 Erosion and dilation
				12.1.3 Opening and closing
				12.1.4 Border extraction
				12.1.5 Region filling
				12.1.6 Component labeling and connected component analysis
				12.1.7 The hit-or-miss operator
				12.1.8 Skeletonization
				12.1.9 Pruning
			12.2 Gray-scale morphology
				12.2.1 The point set of a gray-scale image
				12.2.2 Erosion and dilation
				12.2.3 Opening and closing
				12.2.4 Edge detection
			12.3 Bibliography
	Chapter 13 Texture Analysis
		13 Texture Analysis
			13.1 Types of textures
				13.1.1 Structural texture type
				13.1.2 Structural-statistical texture type
				13.1.3 Statistical texture type
			13.2 Visual inspection tasks regarding textures
			13.3 Model-based texture analysis
				13.3.1 Analysis of structural textures
				13.3.2 Analysis of structural-statistical textures
				13.3.3 Autoregressive models for analyzing statistical textures
				13.3.4 Separation of line textures
			13.4 Feature-based texture analysis
				13.4.1 Basic statistical texture features
				13.4.2 Co-occurrence matrix
				13.4.3 Histogram of oriented gradients
				13.4.4 Run-length analysis
				13.4.5 Laws’ texture energy measures
				13.4.6 Local binary patterns
			13.5 Bibliography
	Chapter 14 Detection
		14 Detection
			14.1 Detection of known objects by linear filters
				14.1.1 Unknown background
				14.1.2 White noise as background
				14.1.3 Correlated, weakly stationary noise as background
				14.1.4 Discrete formulation of the matched filter
			14.2 Detection of unknown objects (defects)
			14.3 Detection of straight lines
				14.3.1 The Radon transform
				14.3.2 Detection of line-shaped structures
				14.3.3 The Hough transform for the detection of lines
				14.3.4 The Hough transform for the detection of curves
				14.3.5 The generalized Hough transform
				14.3.6 Implicit shape models
			14.4 Corner detection
			14.5 Bibliography
	Chapter 15 Image Pyramids, theWavelet Transform and
Multiresolution Analysis
		15 Image Pyramids, the Wavelet Transform and Multiresolution Analysis
			15.1 Image pyramids
				15.1.1 Gaussian pyramid
				15.1.2 Laplacian pyramid
				15.1.3 Pyramid linking
			15.2 Wavelets
				15.2.1 Continuous wavelet transform
				15.2.2 Discretization of the wavelet transform
			15.3 Multiresolution analysis
			15.4 The fast wavelet transform
			15.5 The two-dimensional wavelet transform
			15.6 Scale-invariant features
			15.7 Bibliography
Part III Appendix
	Appendix A Mathematical Foundations
		A Mathematical Foundations
			A.1 The intercept theorem
			A.2 Inverse problems
			A.3 Bibliography
		Appendix B The Fourier Transform
			B The Fourier Transform
				B.1 The one-dimensional Fourier transform
					B.1.1 Definition
					B.1.2 Properties and characteristics
					B.1.3 Correspondences
				B.2 The n-dimensional Fourier transform
					B.2.1 Definition
					B.2.2 Correspondences of the two-dimensional Fourier transform
				B.3 The discrete Fourier transform
List of Symbols
List of Abbreviations
Index