Digital Image Processing, 4/e (GE-Paperback)

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Digital Image Processing, 4/e (GE-Paperback)

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內容描述

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 Processinghas 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.

目錄大綱

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