Digital Image Processing Using MATLAB 4th edition by Rafael Gonzalez, Richard Eugene Woods 9780134734804 0134734807

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ISBN 10: 0134734807
ISBN 13: 9780134734804 
Author: Rafael C. Gonzalez; Richard Eugene Woods

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. 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 

Digital Image Processing Using MATLAB 4th Table of contents:

Chapter 1: Introduction

• Preview
  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
• Summary, References, and Further Reading

Chapter 2: Digital Image Fundamentals

• Preview
  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
    • Sample Spaces, Events, and Probability
    • The Sum (Addition) Rule of Probability
    • Conditional Probability
    • Independence
    • The Law of Total Probability
    • Bayes’ Rule
    • Random Variables
    • Probability Functions for Discrete Random Variables
    • Some Important Probability Mass Functions
    • Estimating Discrete Probability Functions from Sample Data
    • Expected Value and Moments of Discrete Random Variables
    • Continuous Random Variables
    • The Uniform and Gaussian Probability Density Functions
    • Expected Values and Moments of Continuous Random Variables
    • Estimating the Mean, Variance, and Higher-Order Moments from Sample Data
    • Multivariate Random Variables
    • The Multivariate Gaussian PDF
    • Estimating the Parameters of the Multivariate Gaussian PDF
• Summary, References, and Further Reading
• Problems
• Projects

Chapter 3: Intensity Transformations and Spatial Filtering

• Preview
  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
• Summary, References, and Further Reading
• Problems
• Projects

Chapter 4: Filtering in the Frequency Domain

• Preview
  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)
• Summary, References, and Further Reading
• Problems
• Projects

Chapter 5: Image Restoration and Reconstruction

• Preview
  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
• Summary, References, and Further Reading
• Problems
• Projects

Chapter 6: Wavelet and Other Image Transforms

• Preview
  6.1 Preliminaries
  6.2 Matrix-Based Transforms
  • Rectangular Arrays
  • Complex Orthonormal Basis Vectors
  • Real Biorthonormal Basis Vectors
    6.3 Correlation
    6.4 Basis Functions in the Time-Frequency Plane
    6.5 Basis Images
    6.6 Fourier-Related Transforms

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