Digital Image Processing, Global Edition

Series
Pearson
Author
Rafael C. Gonzalez / Richard E. Woods  
Publisher
Pearson
Cover
Softcover
Edition
4
Language
English
Total pages
1024
Pub.-date
October 2017
ISBN13
9781292223049
ISBN
1292223049
Related Titles



Description

For courses in Image Processing and Computer Vision.

For 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 is based on an extensive survey of faculty, students, and independent readers in 5 institutions from 3 countries. Their feedback led to epanded or new coverage of topics such as deep learning and deep neural networks, including convolutional neural nets, the scale-invariant feature transform (SIFT), maimally-stable etremal regions (MSERs), graph cuts, k-means clustering and superpiels, active contours (snakes and level sets), and eact histogram matching. Major improvements were made in reorganising the material on image transforms into a more cohesive presentation, and in the discussion of spatial kernels and spatial filtering.

Features

  • Timely, highly readable, and heavily illustrated with numerous examples of practical significance.
    • NEW! This edition contains 5 new images, 35 new drawings, and new exercises.
  • Focuses on the fundamental material whose scope of application is not limited to the solution of specialised problems
  • Updated with feedback from an extensive survey that involved faculty, students, and independent readers of the book in 5 institutions from 3 countries.
    • UPDATED! A complete update of the image pattern recognition chapter to incorporate new material on deep neural networks, back propagation, deep learning, and, especially, deep convolutional neural networks.
    • EXPANDED! Coverage of feature extraction, including the Scale Invariant Feature Transform SIFT, MSERs, and corner detection.
    • NEW! Coverage of graph cuts and their application to segmentation.
    • NEW! A discussion of superpiels and their use in region segmentation.
    • NEW! An introduction to segmentation using active contours (snakes and level sets).
    • NEW! Material related to each histogram matching.
    • EXPANDED! Coverage of the fundamentals of spatial filtering, image transforms, and finite differences with a focus on edge detection.
  • NEW! Two new chapters:
    • A chapter dealing with active contours for image segmentation, including snakes and level sets.
    • A chapter that brings together wavelets, several new transforms, and many of the image transforms that were scattered throughout the book.
  • NEW! MATLAB projects, located at the end of every chapter and are structured in a unique way that gives instructors significant flexibility in how projects are assigned.
    • The MATLAB functions required to solve all the projects in the book are provided in executable, p-code format which makes it possible for projects to be assigned solely for the purpose of experimenting with image processing concepts, without having to write a single line of code.

New to this Edition

About the Book

  • A complete update of the image pattern recognition chapter to incorporate new material, including deep neural networks, backpropagation, deep learning, and, especially, deep convolutional neural networks.
  • Expanded coverage of feature extraction, including maximally stable extremal regions, and the Scale Invariant Feature Transform (SIFT).
  • A discussion of superpixels and their use in region segmentation.
  • Coverage of graph cuts and their application to segmentation.
  • New material related to histogram matching.
  • Expanded coverage of the fundamentals of spatial filtering.
  • A more comprehensive and cohesive coverage of image transforms.
  • A more complete presentation of finite differences, with a focus on edge detection.
  • More homework problems at the end of the chapters.
  • More examples.

Content updates

· Chapter 1: Some figures were updated and parts of the text were rewritten to correspond to changes in later chapters.

· Chapter 2: A new section dealing with random numbers and probability, with an emphasis on their application to image processing. Many sections and examples were rewritten for clarity.

· Chapter 3: A new section on exact histogram matching, a discussion on separable filter kernels, expanded coverage on the properties of lowpass Gaussian kernels, and highpass, bandreject, and bandpass filters.

· Chapter 4: Several sections were revised to improve the clarity of presentation.

· Chapter 5: Clarifications and a few corrections in notation.

· Chapter 6: Material dealing with color image processing was moved to this chapter. Several sections were clarified, and the explanation of the CMY and CMYK color models was expanded.

· Chapter 7: A new chapter that brings together wavelets, several new transforms, and many of the image transforms that were scattered throughout the book. The emphasis of this chapter is on a cohesive presentation of these transforms from a unified point of view.

· Chapter 8: Numerous clarifications and minor improvements to the presentation.

· Chapter 9: A complete rewrite of several sections, including redrafting of several line drawings.

· Chapter 10: Several sections were rewritten for clarity. Updated the chapter by adding coverage of finite differences, K-means clustering, superpixels, and graph cuts.

· Chapter 11: Updated with numerous topics, improvements in the clarity of presentation, added coverage of slope change codes, expanded explanation of skeletons, medial axes, and the distance transform, and new basic descriptors of compactness, circularity, and eccentricity. New material includes coverage of the Harris-Stephens corner detector, and a presentation of maximally stable extremal regions. A major addition to the chapter is a comprehensive discussion dealing with the Scale-Invariant Feature Transform (SIFT).

· Chapter 123: Now includes coverage of deep convolutional neural networks, an extensive rewrite of neural networks, deep learning, and a comprehensive discussion on fully-connected, deep neural networks that includes derivation of backpropagation starting from basic principles.

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
  • 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
  • 2.2 Light and the Electromagnetic Spectrum
  • 2.3 Image Sensing and Acquisition
  • 2.4 Image Sampling and Quantization
  • 2.5 Some Basic Relationships Between Pixels
  • 2.6 Introduction to the Basic Mathematical Tools Used in Digital Image Processing
  • 3 Intensity Transformations and Spatial Filtering
  • 3.1 Background
  • 3.2 Some Basic Intensity Transformation Functions
  • 3.3 Histogram Processing
  • 3.4 Fundamentals of Spatial Filtering
  • 3.5 Smoothing (Lowpass) Spatial Filters
  • 3.6 Sharpening (Highpass) Spatial Filters
  • 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
  • 4 Filtering in the Frequency Domain
  • 4.1 Background
  • 4.2 Preliminary Concepts
  • 4.3 Sampling and the Fourier Transform of Sampled Functions
  • 4.4 The Discrete Fourier Transform of One Variable
  • 4.5 Extensions to Functions of Two Variables
  • 4.6 Some Properties of the 2-D DFT and IDFT
  • 4.7 The Basics of Filtering in the Frequency Domain
  • 4.8 Image Smoothing Using Lowpass Frequency Domain Filters
  • 4.9 Image Sharpening Using Highpass Filters
  • 4.10 Selective Filtering
  • 4.11 The Fast Fourier Transform
  • 5 Image Restoration and Reconstruction
  • 5.1 A Model of the Image Degradation/Restoration Process
  • 5.2 Noise Models
  • 5.3 Restoration in the Presence of Noise Only—Spatial Filtering
  • 5.4 Periodic Noise Reduction Using Frequency Domain Filtering
  • 5.5 Linear, Position-Invariant Degradations
  • 5.6 Estimating the Degradation Function
  • 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
  • 6 Wavelet and Other Image Transforms
  • 6.1 Preliminaries
  • 6.2 Matrix-based Transforms
  • 6.3 Correlation
  • 6.4 Basis Functions in the Time-Frequency Plane
  • 6.5 Basis Images
  • 6.6 Fourier-Related Transforms
  • 6.7 Walsh-Hadamard Transforms
  • 6.8 Slant Transform
  • 6.9 Haar Transform
  • 6.10 Wavelet Transforms
  • 7 Color Image Processing
  • 7.1 Color Fundamentals
  • 7.2 Color Models
  • 7.3 Pseudocolor Image Processing
  • 7.4 Basics of Full-Color Image Processing
  • 7.5 Color Transformations
  • 7.6 Color Image Smoothing and Sharpening
  • 7.7 Using Color in Image Segmentation
  • 7.8 Noise in Color Images
  • 7.9 Color Image Compression
  • 8 Image Compression and Watermarking
  • 8.1 Fundamentals
  • 8.2 Huffman Coding
  • 8.3 Golomb Coding
  • 8.4 Arithmetic Coding
  • 8.5 LZW Coding
  • 8.6 Run-length Coding