Digital Image Processing

Item Information
Item#: 9780133356724
Edition 04
Author Gonzalez & Woods
Cover Hardback
On Hand 1
On Order 0

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 cele
ates 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 

Table of Contents
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