THE IMAGE PROCESSING HANDBOOK. 7TH EDITION

Introduction
About this text
A word of caution
A personal note

Acquiring Images
Human reliance on images
Extracting information
Video cameras
CCD cameras
CMOS detectors
Camera artifacts and limitations
Color cameras
Camera resolution
Electronics and bandwidth limitations
Handling color data
Color encoding
Other image sources
Pixels
Tonal resolution
The image contents
Camera limitations
Noise
High-depth images
Focusing
Color displays
Image types
Multiple images
Imaging requirements

Printing and Storage
Hard copies
Halftoning
Dots on paper
Color printing
Adding black—CMYK
Printing hardware
Film recorders
Presentation tools
File storage
Storage media
Magnetic recording
Databases for images
Searching by content
Browsing and thumbnails
File formats
Lossless coding
Reduced color palettes
JPEG compression
Wavelet compression
Fractal compression
Digital movies

Human Vision
What we see and why
Recognition
Technical specs
Seeing color
Acuity
What the eye tells the brain
Spatial comparisons
Local to global hierarchies
Grouping
It’s about time
The third dimension
How versus what
Seeing what isn’t there, and vice versa
Image compression
A world of light
Size matters
Shape (whatever that means)
Context
Arrangements must be made
Seeing is believing
Learning more

Correcting Imaging Defects
Color adjustments
Hue, saturation, intensity
Other spaces
Color correction
Noisy images
Neighborhood averaging
Gaussian smoothing
Neighborhood ranking
The color median
More median filters
Weighted, conditional, and adaptive neighborhoods
Other neighborhood noise reduction methods
Defect removal, maximum entropy, and maximum likelihood
Nonuniform illumination
Fitting a background function
Rank leveling
Color images
Nonplanar views
Computer graphics
Geometric distortion
Alignment
Interpolation
Morphing

Image Enhancement in the Spatial Domain
Purposes for enhancement
Contrast expansion
False color lookup tables (LUTs)
Contrast manipulation
Histogram equalization
Contrast in color images
Local equalization
Laplacian sharpening
The unsharp mask
Derivatives
Edges and gradients
Edge orientation
More edge detectors
Rank-based methods
Texture
Implementation notes
Image math
Subtracting images
Multiplication and division
Principal component analysis
Principal component analysis for contrast enhancement
Other image combinations
Cross-correlation

Processing Images in Frequency Space
About frequency space
The Fourier transform
Fourier transforms of simple functions
Moving to two dimensions
Frequencies and spacings
Preferred orientation
Texture and fractals
Removing selected frequencies
Periodic noise removal
Selection of periodic information
Convolution
Deconvolution
Noise and Wiener deconvolution
Other deconvolution methods
Additional notes on deconvolution
Template matching and correlation
Autocorrelation
Wavelets

Segmentation and Thresholding
Brightness thresholding
Automatic settings
Multiband images
Color thresholding
Thresholding from texture
Multiple thresholding criteria
Textural orientation
Region boundaries
Noise and overlaps
Selecting smooth boundaries
Conditional histograms
Boundary lines
Contours
Cluster analysis
More segmentation methods
Image representation

Processing Binary Images
Boolean operations
Combining Boolean operations
Masks
From pixels to features
Filling holes
Measurement grids
Boolean logic with features
Selecting features by location
Double thresholding
Erosion and dilation
Opening and closing
Isotropy
Measurements using erosion and dilation
Extension to grayscale images
Neighborhood parameters
Examples of use
Euclidean distance map
Watershed segmentation
Ultimate eroded points
Skeletons
Topology
Boundary lines
Combining skeleton and Euclidean distance map

Image Measurements
Photogrammetry
Comparisons
Global measurements
Volume
Surface area
Grain size
Multiple surfaces
Length
Thickness
Sampling strategies
Determining number
Curvature, connectivity, and the Disector
Anisotropy and gradients
Size distribution
Classical stereology (unfolding)

Feature Measurements
Brightness measurements
Density
Brightness profiles
Color values
Determining location
Orientation
Neighbor relationships
Separation distance
Alignment
The linear Hough transform
The circular Hough transform
Counting
Special counting procedures
Feature size
Circles and ellipses
Caliper dimensions
Perimeter

Characterizing Shape
Describing shape
Dimensionless ratios
Effects of orientation
"Like a circle"
An example: Leaves
Topology and the skeleton
Boundaries
Shock graphs
Fractal dimension
Measurement techniques
Harmonic analysis
Chain code
An example: Arrow points
Wavelets
Moments
An example: Dandelion
Zernike moments
Landmarks

Correlation, Classification, Identification, and Matching
A variety of purposes
Matching
Cross-correlation
Curvature scale space
Classification
Distributions and decision points
Linear discriminant analysis (LDA) and principal component analysis (PCA)
Class definition
Unsupervised learning
Are groups different?
Neural nets
k-Nearest neighbors
Parametric description
Bayesian statistics
A comparison
Harmonic analysis and invariant moments
Species examples
Correlation
Landmark data

3D Imaging
More than two dimensions
Volume imaging versus sections
Serial sections
Removing layers
Reconstruction
Confocal microscopy
Stereo viewing
Tomography
Tomographic reconstruction
Reconstruction artifacts
Algebraic reconstruction
Maximum entropy
Imaging geometries
Other signals
Beam hardening and other issues
3D tomography
Dual energy methods
Microtomography
3D reconstruction and visualization
Slices and surfaces
Marching cubes
Volumetric displays
Ray tracing

3D Processing and Measurement
Processing voxel arrays
When the z-axis is different
Multiple image sets
Thresholding and segmentation
Morphological operations and structural measurements
Skeletons
Surface and volume
Quantitative use of reconstructions
Methods for object measurements
Size
Examples of object measurements
Other object measurements
Limitations
Industrial applications
Comparison to stereological measurements
Spherical harmonics, wavelets, and fractal dimension
Other applications and future possibilities

Imaging Surfaces
Producing surfaces
Imaging by physical contact
Noncontacting measurements
Shape from shading and polynomial texture map
Microscopy of surfaces
Stereoscopy
Matching points
Composition imaging
Processing of range images
Processing of composition maps
Data presentation and visualization
Surface rendering
Measurements
Profiles
Representing elevation data
The surface measurement suite
Hybrid properties
Topographic analysis
Fractal dimensions

References

Consistently rated as the best overall introduction to computer-based image processing, The Image Processing Handbook covers two-dimensional (2D) and three-dimensional (3D) imaging techniques, image printing and storage methods, image processing algorithms, image and feature measurement, quantitative image measurement analysis, and more.
Incorporating image processing and analysis examples at all scales, from nano- to astro-, this Seventh Edition:
• Features a greater range of computationally intensive algorithms than previous versions
• Provides better organization, more quantitative results, and new material on recent developments
• Includes completely rewritten chapters on 3D imaging and a thoroughly revamped chapter on statistical analysis
• Contains more than 1700 references to theory, methods, and applications in a wide variety of disciplines
• Presents 500+ entirely new figures and images, with more than two-thirds appearing in color

The Image Processing Handbook, Seventh Edition delivers an accessible and up-to-date treatment of image processing, offering broad coverage and comparison of algorithms, approaches, and outcomes.

Authors
• John C. Russ has used image processing and analysis as a principal tool for understanding and characterizing the structure and function of materials throughout his more than 50-year career as a scientist and educator. Much of Russ' research work has been concerned with the microstructure and surface topography of metals and ceramics. He has received funding for his research from government agencies and from industry. Teaching the principles and methods involved to several thousand students—in addition to consulting for many industrial clients—has further broadened Dr. Russ’ experience and the scope of applications for image processing and analysis. He continues to write and consult for a variety of companies (and to provide expert testimony in criminal and civil cases). He also still teaches image processing and analysis workshops worldwide and reviews publications and funding proposals.
• F. Brent Neal is a scientist and industrial researcher with Milliken Research Corporation, where he currently leads the central materials characterization and analytical chemistry facility. In this role, he leads efforts in technology and product development through deep understanding of materials performance. He has three patents issued or pending based on his work in polymer-matrix composites. Prior to his tenure at Milliken Research Corporation, he consulted and developed bespoke software for quantitative image analysis. He received his Ph.D in solid-state physics from Louisiana State University in 2002. Over the course of his career, he has measured and analyzed images from many different fields and his experience in materials characterization and measurement has been applied everywhere from the lab bench to manufacturing plants.