For courses in Image Processing and Computer Vision.
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 students and faculty containing, solutions, image databases, and sample code.
Support materials for this title can be found here: http://www.imageprocessingplace.com
Provide an introduction to basic concepts and methodologies applicable to digital image processing
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About the Book
Content updates
1 Introduction
1.1 What is Digital Image Processing?
1.2 The Origins of Digital Image Processing
1.3 Examples of Fields that Use Digital Image Processing
Gamma-Ray Imaging
X-Ray Imaging
Imaging in the Ultraviolet Band
Imaging in the Visible and Infrared Bands
Imaging in the Microwave Band
Imaging in the Radio Band
Other Imaging Modalities
1.4 Fundamental Steps in Digital Image Processing
1.5 Components of an Image Processing System
2 Digital Image Fundamentals
2.1 Elements of Visual Perception
Structure of the Human Eye
Image Formation in the Eye
Brightness Adaptation and Discrimination
2.2 Light and the Electromagnetic Spectrum
2.3 Image Sensing and Acquisition
Image Acquisition Using a Single Sensing Element
Image Acquisition Using Sensor Strips
Image Acquisition Using Sensor Arrays
A Simple Image Formation Model
2.4 Image Sampling and Quantization
Basic Concepts in Sampling and Quantization
Representing Digital Images
Linear vs. Coordinate Indexing
Spatial and Intensity Resolution
Image Interpolation
2.5 Some Basic Relationships Between Pixels
Neighbors of a Pixel
Adjacency, Connectivity, Regions, and Boundaries
Distance Measures
2.6 Introduction to the Basic Mathematical Tools Used in Digital Image Processing
Elementwise versus Matrix Operations
Linear versus Nonlinear Operations
Arithmetic Operations
Set and Logical Operations
Basic Set Operations
Logical Operations
Fuzzy Sets
Spatial Operations
Single-Pixel Operations
Neighborhood Operations
Geometric Transformations
Image Registration
Vector and Matrix Operations
Image Transforms
Probability and Random Variables
3 Intensity Transformations and Spatial Filtering
3.1 Background
The Basics of Intensity Transformations and Spatial Filtering
About the Examples in this Chapter
3.2 Some Basic Intensity Transformation Functions
Image Negatives
Log Transformations
Power-Law (Gamma) Transformations
Piecewise Linear Transformation Functions
Contrast Stretching
Intensity-Level Slicing
Bit-Plane Slicing
3.3 Histogram Processing
Histogram Equalization
Histogram Matching (Specification)
Exact Histogram Matching (Specification)
Foundation
Ordering
Computing the neighborhood averages and extracting the K-tuples:
Exact Histogram Specification Algorithm
Local Histogram Processing
Using Histogram Statistics for Image Enhancement
3.4 Fundamentals of Spatial Filtering
The Mechanics of Linear Spatial Filtering
Spatial Correlation and Convolution
Separable Filter Kernels
Some Important Comparisons Between Filtering in the Spatial and Frequency Domains
A Word about how Spatial Filter Kernels are Constructed
3.5 Smoothing (Lowpass) Spatial Filters
Box Filter Kernels
Lowpass Gaussian Filter Kernels
Order-Statistic (Nonlinear) Filters
3.6 Sharpening (Highpass) Spatial Filters
Foundation
Using the Second Derivative for Image Sharpening—The Laplacian
Unsharp Masking and Highboost Filtering
Using First-Order Derivatives for Image Sharpening—The Gradient
3.7 Highpass, Bandreject, and Bandpass Filters from Lowpass Filters
3.8 Combining Spatial Enhancement Methods
3.9 Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering
Introduction
Principles of Fuzzy Set Theory
Definitions
Some Common Membership Functions
Using Fuzzy Sets
Using Fuzzy Sets for Intensity Transformations
Using Fuzzy Sets for Spatial Filtering
4 Filtering in the Frequency Domain
4.1 Background
A Brief History of the Fourier Series and Transform
About the Examples in this Chapter
4.2 Preliminary Concepts
Complex Numbers
Fourier Series
Impulses and their Sifting Properties
The Fourier Transform of Functions of One Continuous Variable
Convolution
4.3 Sampling and the Fourier Transform of Sampled Functions
Sampling
The Fourier Transform of Sampled Functions
The Sampling Theorem
Aliasing
Function Reconstruction (Recovery) from Sampled Data
4.4 The Discrete Fourier Transform of One Variable
Obtaining the DFT from the Continuous Transform of a Sampled Function
Relationship Between the Sampling and Frequency Intervals
4.5 Extensions to Functions of Two Variables
The 2-D Impulse and Its Sifting Property
The 2-D Continuous Fourier Transform Pair
2-D Sampling and the 2-D Sampling Theorem
Aliasing in Images
Extensions from 1-D Aliasing
Image Resampling and Interpolation
Aliasing and Moiré Patterns
The 2-D Discrete Fourier Transform and Its Inverse
4.6 Some Properties of the 2-D DFT and IDFT
Relationships Between Spatial and Frequency Intervals
Translation and Rotation
Periodicity
Symmetry Properties
Fourier Spectrum and Phase Angle
The 2-D Discrete Convolution Theorem
Summary of 2-D Discrete Fourier Transform Properties
4.7 The Basics of Filtering in the Frequency Domain
Additional Characteristics of the Frequency Domain
Frequency Domain Filtering Fundamentals
Summary of Steps for Filtering in the Frequency Domain
Correspondence Between Filtering in the Spatial and
Frequency Domains
4.8 Image Smoothing Using Lowpass Frequency Domain Filters
Ideal Lowpass Filters
Gaussian Lowpass Filters
Butterworth Lowpass Filters
Additional Examples of Lowpass Filtering
4.9 Image Sharpening Using Highpass Filters
Ideal, Gaussian, and Butterworth Highpass Filters from Lowpass Filters
The Laplacian in the Frequency Domain
Unsharp Masking, High-boost Filtering, and High-Frequency-Emphasis Filtering
Homomorphic Filtering
4.10 Selective Filtering
Bandreject and Bandpass Filters
Notch Filters
4.11 The Fast Fourier Transform
Separability of the 2-D DFT
Computing the IDFT Using a DFT Algorithm
The Fast Fourier Transform (FFT)
5 Image Restoration and Reconstruction
5.1 A Model of the Image Degradation/Restoration Process
5.2 Noise Models
Spatial and Frequency Properties of Noise
Some Important Noise Probability Density Functions
Gaussian Noise
Rayleigh Noise
Erlang (Gamma) Noise
Exponential Noise
Uniform Noise
Salt-and-Pepper Noise
Periodic Noise
Estimating Noise Parameters
5.3 Restoration in the Presence of Noise Only—Spatial Filtering
Mean Filters
Arithmetic Mean Filter
Geometric Mean Filter
Harmonic Mean Filter
Contraharmonic Mean Filter
Order-Statistic Filters
Median Filter
Max and Min Filters
Midpoint Filter
Alpha-Trimmed Mean Filter
Adaptive Filters
Adaptive, Local Noise Reduction Filter
Adaptive Median Filter
5.4 Periodic Noise Reduction Using Frequency Domain Filtering
More on Notch Filtering
Optimum Notch Filtering
5.5 Linear, Position-Invariant Degradations
5.6 Estimating the Degradation Function
Estimation by Image Observation
Estimation by Experimentation
Estimation by Modeling
5.7 Inverse Filtering
5.8 Minimum Mean Square Error (Wiener) Filtering
5.9 Constrained Least Squares Filtering
5.10 Geometric Mean Filter
5.11 Image Reconstruction from Projections
Introduction
Principles of X-ray Computed Tomography (CT)
Projections and the Radon Transform
Backprojections
The Fourier-Slice Theorem
Reconstruction Using Parallel-Beam Filtered Backprojections
Reconstruction Using Fan-Beam Filtered Backprojections
6 Wavelet and Other Image Transforms
6.1 Preliminaries
6.2 Matrix-based Transforms
Rectangular Arrays
Complex Orthonormal Basis Vectors
Biorthonormal Basis Vectors
6.3 Correlation
6.4 Basis Functions in the Time-Frequency Plane
6.5 Basis Images
6.6 Fourier-Related Transforms
The Discrete hartley Transform
The Discrete Cosine Transform
The Discrete Sine Transform
6.7 Walsh-Hadamard Transforms
6.8 Slant Transform
6.9 Haar Transform
6.10 Wavelet Transforms
Scaling Functions
Wavelet Functions
Wavelet Series Expansion
Discrete Wavelet Transform in One Dimension
The Fast Wavelet Transform
Wavelet Transforms in Two Dimensions
Wavelet Packets
7 Color Image Processing
7.1 Color Fundamentals
7.2 Color Models
The RGB Color Model
The CMY and CMYK Color Models
The HSI Color Model
Converting Colors from RGB to HSI
Converting Colors from HSI to RGB
Manipulating HSI Component Images
A Device Independent Color Model
7.3 Pseudocolor Image Processing
Intensity Slicing and Color Coding
Intensity to Color Transformations
7.4 Basics of Full-Color Image Processing
7.5 Color Transformations
Formulation
Color Complements
Color Slicing
Tone and Color Corrections
Histogram Processing of Color Images
7.6 Color Image Smoothing and Sharpening
Color Image Smoothing
Color Image Sharpening
7.7 Using Color in Image Segmentation
Segmentation in HSI Color Space
Segmentation in RGB Space
Color Edge Detection
7.8 Noise in Color Images
7.9 Color Image Compression
8 Image Compression and Watermarking
8.1 Fundamentals
Coding Redundancy
Spatial and Temporal Redundancy
Irrelevant Information
Measuring Image Information
Shannon’s First Theorem
Fidelity Criteria
Image Compression Models
The Encoding or Compression Process
The Decoding or Decompression Process
Image Formats, Containers, and Compression Standards
8.2 Huffman Coding
8.3 Golomb Coding
8.4 Arithmetic Coding
Adaptive context dependent probability estimates
8.5 LZW Coding
8.6 Run-length Coding
One-dimensional CCITT compression
Two-dimensional CCITT compression
8.7 Symbol-based Coding
JBIG2 compression
8.8 Bit-plane Coding
8.9 Block Transform Coding
Transform selection
Subimage size selection
Bit allocation
Zonal Coding Implementation
Threshold Coding Implementation
JPEG
8.10 Predictive Coding
Lossless predictive coding
Motion compensated prediction residuals
Lossy predictive coding
Optimal predictors
Optimal quantization
8.11 Wavelet Coding
Wavlet selection
Decomposition level selection
Quantizer design
JPEG-2000
8.12 Digital Image Watermarking
9 Morphological Image Processing
9.1 Preliminaries
9.2 Erosion and Dilation
Erosion
Dilation
Duality
9.3 Opening and Closing
9.4 The Hit-or-Miss Transform
9.5 Some Basic Morphological Algorithms
Boundary Extraction
Hole Filling
Extraction of Connected Components
Convex Hull
Thinning
Thickening
Skeletons
Pruning
9.6 Morphological Reconstruction
Geodesic Dilation and Erosion
Morphological Reconstruction by Dilation and by Erosion
Sample Applications
Opening by Reconstruction
Automatic Algorithm for Filling Holes
Border Clearing
9.7 Summary of Morphological Operations on Binary Images
9.8 Grayscale Morphology
Grayscale Erosion and Dilation
Grayscale Opening and Closing
Some Basic Grayscale Morphological Algorithms
Morphological Smoothing
Morphological Gradient
Top-Hat and Bottom-Hat Transformations
Granulometry
Textural Segmentation
Grayscale Morphological Reconstruction
10 Image Segmentation I: Edge Detection,
Thresholding, and Region Detection
10.1 Fundamentals
10.2 Point, Line, and Edge Detection
Background
Detection of Isolated Points
Line Detection
Edge Models
Basic Edge Detection
The Image Gradient and Its Properties
Gradient Operators
Combining the Gradient with Thresholding
More Advanced Techniques for Edge Detection
The Marr-Hildreth Edge Detector
The Canny Edge Detector
Linking Edge Points
Local Processing
Global Processing Using the Hough Transform
10.3 Thresholding
Foundation
The Basics of Intensity Thresholding
The Role of Noise in Image Thresholding
The Role of Illumination and Reflectance in Image Thresholding
Basic Global Thresholding
Optimum Global Thresholding Using Otsu’s Method
Using Image Smoothing to Improve Global Thresholding
Using Edges to Improve Global Thresholding
Multiple Thresholds
Variable Thresholding
Variable Thresholding Based on Local Image Properties
Variable Thresholding Based on Moving Averages
10.4 Segmentation by Region Growing and by Region Splitting and Merging
Region Growing
Region Splitting and Merging
10.5 Region Segmentation Using Clustering and Superpixels
Region Segmentation using K-Means Clustering
Region Segmentation using Superpixels
SLIC Superpixel Algorithm
Specifying the Distance Measure
10.6 Region Segmentation Using Graph Cuts
Images as Graphs
Minimum Graph Cuts
Computing Minimal Graph Cuts
Graph Cut Segmentation Algorithm
10.7 Segmentation Using Morphological Watersheds
Background
Dam Construction
Watershed Segmentation Algorithm
The Use of Markers
10.8 The Use of Motion in Segmentation
Spatial Techniques
A Basic Approach
Accumulative Differences
Establishing a Reference Image
Frequency Domain Techniques
11 Image Segmentation II: Active Contours: Snakes and Level Sets
11.1 Background
11.2 Image Segmentation Using Snakes
Explicit (Parametric) Representation of Active Contours
Derivation of the Fundamental Snake Equation
Iterative Solution of the Snake Equation
External Force Based on the Magnitude of the Image
Gradient (MOG)
External Force Based on Gradient Vector Flow (GVF)
11.3 Segmentation Using Level Sets
Implicit Representation of Active Contours
Derivation of the Level Set Equation
Discrete (Iterative) Solution of The Level Set Equation
Curvature
Specifying, Initializing, and Reinitializing Level Set Functions
Force Functions Based Only on Image Properties
Edge/Curvature-Based Forces
Region/Curvature-Based Forces
Improving the Computational Performance of Level Set Algorithms
12 Feature Extraction
12.1 Background
12.2 Boundary Preprocessing
Boundary Following (Tracing)
Chain Codes
Freeman Chain Codes
Slope Chain Codes
Boundary Approximations Using Minimum-Perimeter Polygons
Foundation
MPP Algorithm
Signatures
Skeletons, Medial Axes, and Distance Transforms
12.3 Boundary Feature Descriptors
Some Basic Boundary Descriptors
Shape Numbers
Fourier Descriptors
Statistical Moments
12.4 Region Feature Descriptors
Some Basic Descriptors
Topological Descriptors
Texture
Statistical Approaches
Spectral Approaches
Moment Invariants
12.5 Principal Components as Feature Descriptors
12.6 Whole-Image Features
The Harris-Stephens Corner Detector
Maximally Stable Extremal Regions (MSERs)
12.7 Scale-Invariant Feature Transform (SIFT)
Scale Space
Detecting Local Extrema
Finding the Initial Keypoints
Improving the Accuracy of Keypoint Locations
Eliminating Edge Responses
Keypoint Orientation
Keypoint Descriptors
Summary of the SIFT Algorithm
13 Image Pattern Classification
13.1 Background
13.2 Patterns and Pattern Classes
Pattern Vectors
Structural Patterns
13.3 Pattern Classification by Prototype Matching
Minimum-Distance Classifier
Using Correlation for 2-D prototype matching
Matching SIFT Features
Matching Structural Prototypes
Matching Shape Numbers
String Matching
13.4 Optimum (Bayes) Statistical Classifiers
Derivation of the Bayes Classifier
Bayes Classifier for Gaussian Pattern Classes
13.5 Neural Networks and Deep Learning
Background
The Perceptron
Multilayer Feedforward Neural Networks
Model of an Artificial Neuron
Interconnecting Neurons to Form a Fully Connected Neural Network
Forward Pass Through a Feedforward Neural Network
The Equations of a Forward Pass
Matrix Formulation
Using Backpropagation to Train Deep Neural Networks
The Equations of Backpropagation
Matrix Formulation
13.6 Deep Convolutional Neural Networks
A Basic CNN Architecture
Basics of How a CNN Operates
Neural Computations in a CNN
Multiple Input Images
The Equations of a Forward Pass Through a CNN
The Equations of Backpropagation Used to Train CNNs
13.7 Some Additional Details of Implementation
Bibliography
Index
Instructor Solutions Manual for Digital Image Processing, 4th Edition
Gonzalez & Woods
©2018
Powerpoints for Digital Image Processing, 4th Edition
Gonzalez & Woods
©2018
Companion Website for Digital Imaging Processing, 4th Edition
Gonzalez & Woods
©2018
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Gonzalez & Woods
©2018 | Pearson | 1192 pp
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.
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