The Image Processing Handbook: 7th Edition (Paperback) book cover

The Image Processing Handbook

7th Edition

By John C. Russ, F. Brent Neal

CRC Press

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Paperback: 9781138747494
pub: 2017-08-02
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Description

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.

Reviews

"With a new co-author (the same Brent Neal who has collaborated with him before in writing the excellent book Measuring Shape), John Russ has again produced a winner—a textbook and reference book that belongs on the shelf, and perhaps on the desk, of anyone involved in digital imaging. Even if you have a copy of one of the previous editions, this is a highly worthwhile addition."

Microscopy and Microanalysis, October 2016

Table of Contents

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-depthimages

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 distancemap

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 principalcomponent analysis (PCA)

Class definition

Unsupervised learning

Are groups different?

Neural nets

k-Nearestneighbors

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

About the 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.

Subject Categories

BISAC Subject Codes/Headings:
TEC007000
TECHNOLOGY & ENGINEERING / Electrical
TEC015000
TECHNOLOGY & ENGINEERING / Imaging Systems
TEC059000
TECHNOLOGY & ENGINEERING / Biomedical