|Digital Image Processing, Global Edition||
Digital Image Processing, Global Edition
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.
Provide an introduction to basic concepts and methodologies applicable to digital image processing
Comprehensive support for both students and instructors
· A companion website is available at http://www.imageprocessingplace.com
o Although Digital Image Processing is a completely self-contained book, the companion website offers additional support in a number of important areas, including solution manuals, errata sheets, tutorials, publications in the field, a list of books, numerous databases, links to related websites, and many other features that complement the book.
· NEW! Student Support Package contains all the original images in the book, answers to selected exercises, and instructions for using a set of utility functions that complement the projects.
· NEW! Faculty Support Package contains solutions to all exercises and projects, teaching suggestions, and all the art in the book in the form of modifiable Powerpoint slides. One support package is made available with every new book, free of charge.
About the Book
· 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.
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
3.2 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
4 Filtering in the Frequency Domain
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 Color Image Processing
6.1 Color Fundamentals
6.2 Color Models
6.3 Pseudocolor Image Processing
6.4 Basics of Full-Color Image Processing
6.5 Color Transformations
6.6 Color Image Smoothing and Sharpening
6.6 Using Color in Image Segmentation
6.8 Noise in Color Images
6.9 Color Image Compression
7 Wavelet and Other Image Transforms
7.2 Matrix-based Transforms
7.4 Basis Functions in the Time-Frequency Plane
7.5 Basis Images
7.7 Fourier-Related Transforms
7.7 Walsh-Hadamard Transforms
7.8 Slant Transform
7.9 Haar Transform
7.10 Wavelet Transforms
8 Image Compression and Watermarking
8.2 Huffman Coding
8.3 Golomb Coding
8.4 Arithmetic Coding
8.5 LZW Coding
8.6 Run-length Coding
8.7 Symbol-based Coding
8.8 Bit-plane Coding
8.9 Block Transform Coding
8.10 Predictive Coding
8.11 Wavelet Coding
8.12 Digital Image Watermarking
9 Morphological Image Processing
9.2 Erosion and Dilation
9.3 Opening and Closing
9.4 The Hit-or-Miss Transform
9.5 Some Basic Morphological Algorithms
9.6 Morphological Reconstruction
9.7 Summary of Morphological Operations on Binary Images
9.8 Grayscale Morphology
10 Image Segmentation
10.2 Point, Line, and Edge Detection
10.4 Segmentation by Region Growing and by Region Splitting and Merging
10.5 Region Segmentation Using Clustering and Superpixels
10.6 Region Segmentation Using Graph Cuts
10.7 Segmentation Using Morphological Watersheds
10.8 The Use of Motion in Segmentation
11 Feature Extraction
11.2 Boundary Preprocessing
11.3 Boundary Feature Descriptors
11.4 Region Feature Descriptors
11.5 Principal Components as Feature Descriptors
11.6 Whole-Image Features
11.7 Scale-Invariant Feature Transform (SIFT)
12 Image Pattern Classification
12.2 Patterns and Pattern Classes
12.3 Pattern Classification by Prototype Matching
12.4 Optimum (Bayes) Statistical Classifiers
12.5 Neural Networks and Deep Learning
12.6 Deep Convolutional Neural Networks
12.7 Some Additional Details of Implementation