Digital Image Processing, Global Edition

**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 www.ImageProcessingPlace.com Front Cover 1 Contents 7 Preface 11 Acknowledgments 14 The Book Website 15 The DIP4E Support Packages 15 About the Authors 16 1 Introduction 19 What is Digital Image Processing? 20 The Origins of Digital Image Processing 21 Examples of Fields that Use Digital Image Processing 25 Fundamental Steps in Digital Image Processing 43 Components of an Image Processing System 46 2 Digital Image Fundamentals 49 Elements of Visual Perception 50 Light and the Electromagnetic Spectrum 56 Image Sensing and Acquisition 59 Image Sampling and Quantization 65 Some Basic Relationships Between Pixels 81 Introduction to the Basic Mathematical Tools Used in Digital Image Processing 85 3 Intensity Transformations and Spatial Filtering 121 Background 122 Some Basic Intensity Transformation Functions 124 Histogram Processing 135 Fundamentals of Spatial Filtering 155 Smoothing (Lowpass) Spatial Filters 166 Sharpening (Highpass) Spatial Filters 177 Highpass, Bandreject, and Bandpass Filters from Lowpass Filters 190 Combining Spatial Enhancement Methods 193 4 Filtering in the Frequency Domain 205 Background 206 Preliminary Concepts 209 Sampling and the Fourier Transform of Sampled Functions 217 The Discrete Fourier Transform of One Variable 227 Extensions to Functions of Two Variables 232 Some Properties of the 2-D DFT and IDFT 242 The Basics of Filtering in the Frequency Domain 262 Image Smoothing Using Lowpass Frequency Domain Filters 274 Image Sharpening Using Highpass Filters 286 Selective Filtering 298 The Fast Fourier Transform 305 5 Image Restoration and Reconstruction 319 A Model of the Image Degradation/Restoration process 320 Noise Models 320 Restoration in the Presence of Noise Only—Spatial Filtering 329 Periodic Noise Reduction Using Frequency Domain Filtering 342 Linear, Position-Invariant Degradations 350 Estimating the Degradation Function 354 Inverse Filtering 358 Minimum Mean Square Error (Wiener) Filtering 360 Constrained Least Squares Filtering 365 Geometric Mean Filter 369 Image Reconstruction from Projections 370 6 Color Image Processing 401 Color Fundamentals 402 Color Models 407 Pseudocolor Image Processing 422 Basics of Full-Color Image Processing 431 Color Transformations 432 Color Image Smoothing and Sharpening 444 Using Color in Image Segmentation 447 Noise in Color Images 454 Color Image Compression 457 7 Wavelet and Other Image Transforms 465 Preliminaries 466 Matrix-based Transforms 468 Correlation 480 Basis Functions in the Time-Frequency Plane 481 Basis Images 485 Fourier-Related Transforms 486 Walsh-Hadamard Transforms 498 Slant Transform 502 Haar Transform 504 Wavelet Transforms 506 8 Image Compression and Watermarking 541 Fundamentals 542 Huffman Coding 555 Golomb Coding 558 Arithmetic Coding 563 LZW Coding 566 Run-length Coding 568 Symbol-based Coding 574 Bit-plane Coding 577 Block Transform Coding 578 Predictive Coding 596 Wavelet Coding 616 Digital Image Watermarking 626 9 Morphological Image Processing 637 Preliminaries 638 Erosion and Dilation 640 Opening and Closing 646 The Hit-or-Miss Transform 650 Some Basic Morphological Algorithms 654 Morphological Reconstruction 669 Summary of Morphological Operations on Binary Images 675 Grayscale Morphology 676 10 Image Segmentation 701 Fundamentals 702 Point, Line, and Edge Detection 703 Thresholding 744 Segmentation by Region Growing and by Region Splitting and Merging 766 Region Segmentation Using Clustering and Superpixels 772 Region Segmentation Using Graph Cuts 779 Segmentation Using Morphological Watersheds 788 The Use of Motion in Segmentation 798 11 Feature Extraction 813 Background 814 Boundary Preprocessing 816 Boundary Feature Descriptors 833 Region Feature Descriptors 842 Principal Components as Feature Descriptors 861 Whole-Image Features 870 Scale-Invariant Feature Transform (SIFT) 883 12 Image Pattern Classification 905 Background 906 Patterns and Pattern Classes 908 Pattern Classification by Prototype Matching 912 Optimum (Bayes) Statistical Classifiers 925 Neural Networks and Deep Learning 933 Deep Convolutional Neural Networks 966 Some Additional Details of Implementation 989 Bibliography 997 Index 1011 Back Cover 1022 "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."--Amazon.com