Digital image processing PDF
Preview Digital image processing
Digital Image Processing Rafael C. Gonzalez University of Tennessee Richard E. Woods Interapptics 330 Hudson Street, New York, NY 10013 Senior Vice President Courseware Portfolio Management: Marcia J. Horton Director, Portfolio Management: Engineering, Computer Science & Global Editions: Julian Partridge Portfolio Manager: Julie Bai Field Marketing Manager: Demetrius Hall Product Marketing Manager: Yvonne Vannatta Marketing Assistant: Jon Bryant Content Managing Producer, ECS and Math: Scott Disanno Content Producer: Michelle Bayman Project Manager: Rose Kernan Operations Specialist: Maura Zaldivar-Garcia Manager, Rights and Permissions: Ben Ferrini Cover Designer: Black Horse Designs Cover Photo: MRI image—Author supplied; Rose—Author supplied; Satellite image of Washington, D.C.—Courtesy of NASA; Bottles—Author supplied; Fingerprint— Courtesy of the National Institute of Standards and Technology; Moon IO of Jupiter— Courtesy of NASA Composition: Richard E. 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These efforts include the development, research, and testing of theories and programs to determine their effectiveness. The author and publisher make no warranty of any kind, expressed or implied, with regard to these programs or the documentation contained in this book. The author and publisher shall not be liable in any event for incidental or consequential damages with, or arising out of, the furnishing, performance, or use of these programs. Pearson Education Ltd., London Pearson Education Singapore, Pte. Ltd Pearson Education Canada, Inc. Pearson Education Japan Pearson Education Australia PTY, Ltd Pearson Education North Asia, Ltd., Hong Kong Pearson Education de Mexico, S.A. de C.V. Pearson Education Malaysia, Pte. Ltd. Pearson Education, Inc., Hoboken MATLAB is a registered trademark of The MathWorks, Inc., 1 Apple Hill Drive, Natick, MA 01760-2098. Library of Congress Cataloging-in-Publication Data Names: Gonzalez, Rafael C., author. | Woods, Richard E. (Richard Eugene), author. Title: Digital Image Processing / Rafael C. Gonzalez, University of Tennessee, Richard E. Woods, Interapptics. Description: New York, NY : Pearson, [2018] | Includes bibliographical references and index. Identifiers: LCCN 2017001581 | ISBN 9780133356724 Subjects: LCSH: Image processing--Digital techniques. Classification: LCC TA1632 .G66 2018 | DDC 621.36/7--dc23 LC record available at https://lccn.loc.gov/2017001581 1 16 ISBN-10: 0-13-335672-8 ISBN-13: 9780133356724 www.pearsonhighered.com Contents Cover Title Page Copyright Dedication Preface ix Acknowledgments xiii The Book Website xiv The DIP4E Support Packages xiv About the Authors xv 1 Introduction 1 What is Digital Image Processing? 2 The Origins of Digital Image Processing 3 Examples of Fields that Use Digital Image Processing 7 Fundamental Steps in Digital Image Processing 25 Components of an Image Processing System 28 2 Digital Image Fundamentals 31 Elements of Visual Perception 32 Light and the Electromagnetic Spectrum 38 Image Sensing and Acquisition 41 Image Sampling and Quantization 47 Some Basic Relationships Between Pixels 63 Introduction to the Basic Mathematical Tools Used in Digital Image Processing 67 3 Intensity Transformations and Spatial Filtering 133 Background 134 Some Basic Intensity Transformation Functions 136 Histogram Processing 147 Fundamentals of Spatial Filtering 177 Smoothing (Lowpass) Spatial Filters 188 Sharpening (Highpass) Spatial Filters 199 Highpass, Bandreject, and Bandpass Filters from Lowpass Filters 212 Combining Spatial Enhancement Methods 216 Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering 217 4 Filtering in the Frequency Domain 249 Background 250 Preliminary Concepts 253 Sampling and the Fourier Transform of Sampled Functions 261 The Discrete Fourier Transform of One Variable 271 Extensions to Functions of Two Variables 276 Some Properties of the 2-D DFT and IDF 286 The Basics of Filtering in the Frequency Domain 306 Image Smoothing Using Lowpass Frequency Domain Filters 318 Image Sharpening Using Highpass Filters 330 Selective Filtering 342 The Fast Fourier Transform 349 5 Image Restoration and Reconstruction 365 A Model of the Image Degradation/Restoration process 366 Noise Models 366 Restoration in the Presence of Noise Only—Spatial Filtering 375 Periodic Noise Reduction Using Frequency Domain Filtering 388 Linear, Position-Invariant Degradations 396 Estimating the Degradation Function 400 Inverse Filtering 404 Minimum Mean Square Error (Wiener) Filtering 406 Constrained Least Squares Filtering 411 Geometric Mean Filter 415 Image Reconstruction from Projections 416 6 Wavelet and Other Image Transforms 451 Preliminaries 452 Matrix-based Transforms 454 Correlation 466 Basis Functions in the Time-Frequency Plane 467 Basis Images 471 Fourier-Related Transforms 472 Walsh-Hadamard Transforms 484 Slant Transform 488 Haar Transform 490 Wavelet Transforms 492 7 Color Image Processing 529 Color Fundamentals 530 Color Models 535 Pseudocolor Image Processing 550 Basics of Full-Color Image Processing 559 Color Transformations 560 Color Image Smoothing and Sharpening 572 Using Color in Image Segmentation 575 Noise in Color Images 582 Color Image Compression 585 8 Image Compression and Watermarking 595 Fundamentals 596 Huffman Coding 609 Golomb Coding 612 Arithmetic Coding 617 LZW Coding 620 Run-length Coding 622 Symbol-based Coding 628 Bit-plane Coding 631 Block Transform Coding 632 Predictive Coding 650 Wavelet Coding 670 Digital Image Watermarking 680 9 Morphological Image Processing 693 Preliminaries 694 Erosion and Dilation 696 Opening and Closing 702 The Hit-or-Miss Transform 706 Some Basic Morphological Algorithms 710 Morphological Reconstruction 725 Summary of Morphological Operations on Binary Images 731 Grayscale Morphology 732 10 Image Segmentation I 761 Fundamentals 762 Point, Line, and Edge Detection 763 Thresholding 804 Segmentation by Region Growing and by Region Splitting and Merging 826 Region Segmentation Using Clustering and Superpixels 832 The Use of Motion in Segmentation 859 11 Image Segmentation II Active Contours: Snakes and Level Sets 877 Background 878 Image Segmentation Using Snakes 878 Segmentation Using Level Sets 902 12 Feature Extraction 953 Background 954 Boundary Preprocessing 956 Boundary Feature Descriptors 973 Region Feature Descriptors 982 Principal Components as Feature Descriptors 1001 Whole-Image Features 1010 Scale-Invariant Feature Transform (SIFT) 1023 13 Image Pattern Classification 1049 Background 1050 Patterns and Pattern Classes 1052 Pattern Classification by Prototype Matching 1056 Optimum (Bayes) Statistical Classifiers 1069 Neural Networks and Deep Learning 1077 Deep Convolutional Neural Networks 1110 Some Additional Details of Implementation 1133 Bibiography 1143 Index 1157
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