Introduce your students to image processing with the industry’s most prized text

For 40 years, Image Processing has been the foundational text for the study of digital image processing. The book is suited for students at the college senior and first-year graduate level with prior background in mathematical analysis, vectors, matrices, probability, statistics, linear systems, and computer programming. As in all earlier editions, the focus of this edition of the book is on fundamentals.

The 4th Edition, which celebrates the book’s 40th anniversary, is based on an extensive survey of faculty, students, and independent readers in 150 institutions from 30 countries. Their feedback led to expanded or new coverage of topics such as deep learning and deep neural networks, including convolutional neural nets, the scale-invariant feature transform (SIFT), maximally-stable extremal regions (MSERs), graph cuts, k-means clustering and superpixels, active contours (snakes and level sets), and exact histogram matching. Major improvements were made in reorganizing the material on image transforms into a more cohesive presentation, and in the discussion of spatial kernels and spatial filtering. Major revisions and additions were made to examples and homework exercises throughout the book. For the first time, we added MATLAB projects at the end of every chapter, and compiled support packages for you and your teacher containing, solutions, image databases, and sample code.

The support materials for this title can be found at www.ImageProcessingPlace.com

Rafael C. Gonzalez received the B.S.E.E. degree from the University of Miami in 1965 and the M.E. and Ph.D. degrees in electrical engineering from the University of Florida, Gainesville, in 1967 and 1970, respectively. He joined the Electrical and Computer Engineering Department at University of Tennessee, Knoxville (UTK) in 1970, where he became Associate Professor in 1973, Professor in 1978, and Distinguished Service Professor in 1984. He served as Chairman of the department from 1994 through 1997. He is currently a Professor Emeritus at UTK.

Gonzalez is the founder of the Image & Pattern Analysis Laboratory and the Robotics & Computer Vision Laboratory at the University of Tennessee. He also founded Perceptics Corporation in 1982 and was its president until 1992. The last three years of this period were spent under a full-time employment contract with Westinghouse Corporation, who acquired the company in 1989.

Under his direction, Perceptics became highly successful in image processing, computer vision, and laser disk storage technology. In its initial ten years, Perceptics introduced a series of innovative products, including: The world's first commercially-available computer vision system for automatically reading the license plate on moving vehicles; a series of large-scale image processing and archiving systems used by the U.S. Navy at six different manufacturing sites throughout the country to inspect the rocket motors of missiles in the Trident II Submarine Program; the market leading family of imaging boards for advanced Macintosh computers; and a line of trillion-byte laserdisc products.

He is a frequent consultant to industry and government in the areas of pattern recognition, image processing, and machine learning. His academic honors for work in these fields include the 1977 UTK College of Engineering Faculty Achievement Award; the 1978 UTK Chancellor's Research Scholar Award; the 1980 Magnavox Engineering Professor Award; and the 1980 M.E. Brooks Distinguished Professor Award. In 1981 he became an IBM Professor at the University of Tennessee and in 1984 he was named a Distinguished Service Professor there. He was awarded a Distinguished Alumnus Award by the University of Miami in 1985, the Phi Kappa Phi Scholar Award in 1986, and the University of Tennessee's Nathan W. Dougherty Award for Excellence in Engineering in 1992.

Honors for industrial accomplishment include the 1987 IEEE Outstanding Engineer Award for Commercial Development in Tennessee; the 1988 Albert Rose Nat'l Award for Excellence in Commercial Image Processing; the 1989 B. Otto Wheeley Award for Excellence in Technology Transfer; the 1989 Coopers and Lybrand Entrepreneur of the Year Award; the 1992 IEEE Region 3 Outstanding Engineer Award; and the 1993 Automated Imaging Association National Award for Technology Development.

Gonzalez is author or co-author of over 100 technical articles, two edited books, and four textbooks in the fields of pattern recognition, image processing and robotics. His books are used in over 500 universities and research institutions throughout the world. He is listed in the prestigious Marquis Who's Who in America, Marquis Who's Who in Engineering, Marquis Who's Who in the World, and in 10 other national and international biographical citations. He ii is the co-holder of two U.S. Patents, and has been an associate editor of the IEEE Transactions on Systems, Man and Cybernetics, and the International Journal of Computer and Information Sciences. He is a member of numerous professional and honorary societies, including Tau Beta Pi, Phi Kappa Phi, Eta Kapp Nu, and Sigma Xi. He is a Fellow of the IEEE.

Richard E. Woods earned his B.S., M.S., and Ph.D. degrees in Electrical Engineering from the University of Tennessee, Knoxville in 1975, 1977, and 1980, respectively. He became an Assistant Professor of Electrical Engineering and Computer Science in 1981 and was recognized as a Distinguished Engineering Alumnus in 1986.

A veteran hardware and software developer, Dr. Woods has been involved in the founding of several high-technology startups, including Perceptics Corporation, where he was responsible for the development of the company’s quantitative image analysis and autonomous decision-making products; MedData Interactive, a high technology company specializing in the development of handheld computer systems for medical applications; and Interapptics, an internet-based company that designs desktop and handheld computer applications.

Dr. Woods currently serves on several nonprofit educational and media-related boards, including Johnson University, and was recently a summer English instructor at the Beijing Institute of Technology. He is the holder of a U.S. Patent in the area of digital image processing and has published two textbooks, as well as numerous articles related to digital signal processing. Dr. Woods is a member of several professional societies, including Tau Beta Pi, Phi Kappa Phi, and the IEEE.

1. Introduction

What Is Digital Image Processing?

The Origins of Digital Image Processing

Examples of Fields that Use Digital Image Processing

Fundamental Steps in Digital Image Processing

Components of an Image Processing System

2. Digital Image Fundamentals

Elements of Visual Perception

Light and the Electromagnetic Spectrum. Image Sensing and Acquisition

Image Sampling and Quantization

Some Basic Relationships Between Pixels

An Introduction to the Mathematical Tools Used in Digital Image Processing

3. Intensity Transformations and Spatial Filtering

Background

Some Basic Intensity Transformation Functions

Histogram Processing. Fundamentals of Spatial Filtering

Smoothing Spatial Filters

Sharpening Spatial Filters

Combining Spatial Enhancement Methods

Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering

4. Filtering in the Frequency Domain

Background

Preliminary Concepts

Sampling and the Fourier Transform of Sampled Functions

The Discrete Fourier Transform (DFT) of One Variable

Extension to Functions of Two Variables

Some Properties of the 2-D Discrete Fourier Transform

The Basics of Filtering in the Frequency Domain

Image Smoothing Using Frequency Domain Filters

Image Sharpening Using Frequency Domain Filters

Selective Filtering

Implementation

5. Image Restoration and Reconstruction

A Model of the Image Degradation/Restoration Process

Noise Models

Restoration in the Presence of Noise Only–Spatial Filtering

Periodic Noise Reduction by Frequency Domain Filtering

Linear, Position-Invariant Degradations. Estimating the Degradation Function

Inverse Filtering

Minimum Mean Square Error (Wiener) Filtering

Constrained Least Squares Filtering. Geometric Mean Filter

Image Reconstruction from Projections.

6. Color Image Processing

Color Fundamentals

Color Models

Pseudocolor Image Processing

Basics of Full-Color Image Processing

Color Transformations. Smoothing and Sharpening

Image Segmentation Based on Color

Noise in Color Images

Color Image Compression

7. Wavelets and Multiresolution Processing

Background

Multiresolution Expansions

Wavelet Transforms in One Dimension

The Fast Wavelet Transform

Wavelet Transforms in Two Dimensions

Wavelet Packets

8. Image Compression

Fundamentals

Some Basic Compression Methods

Digital Image Watermarking

9. Morphological Image Processing

Preliminaries

Erosion and Dilation

Opening and Closing

The Hit-or-Miss Transformation

Some Basic Morphological Algorithms

Gray-Scale Morphology

10. Image Segmentation

Fundamentals

Point, Line, and Edge Detection

Thresholding. Region-Based Segmentation

Segmentation Using Morphological Watersheds

The Use of Motion in Segmentation

11. Representation and Description

Representation

Boundary Descriptors

Regional Descriptors

Use of Principal Components for Description

Relational Descriptors

12. Object Recognition

Patterns and Pattern Classes

Recognition Based on Decision-Theoretic Methods

Structural Methods

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