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Note for Digital Image Processing - DIP By n dasharath

  • Digital Image Processing - DIP
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Library of Congress Cataloging-in-Publication Data on File Vice President and Editorial Director, ECS: Marcia J. Horton Executive Editor: Michael McDonald Associate Editor: Alice Dworkin Editorial Assistant: William Opaluch Managing Editor: Scott Disanno Production Editor: Rose Kernan Director of Creative Services: Paul Belfanti Creative Director: Juan Lopez Art Director: Heather Scott Art Editors: Gregory Dulles and Thomas Benfatti Manufacturing Manager: Alexis Heydt-Long Manufacturing Buyer: Lisa McDowell Senior Marketing Manager: Tim Galligan © 2008 by Pearson Education, Inc. Pearson Prentice Hall Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. No part of this book may be reproduced, in any form, or by any means, without permission in writing from the publisher. Pearson Prentice Hall® is a trademark of Pearson Education, Inc. The authors and publisher of this book have used their best efforts in preparing this book.These efforts include the development, research, and testing of the theories and programs to determine their effectiveness.The authors and publisher make no warranty of any kind, expressed or implied, with regard to these programs or the documentation contained in this book.The authors 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. Printed in the United States of America. 10 9 8 7 6 5 4 3 2 1 ISBN 0-13-168728-x 978-0-13-168728-8 Pearson Education Ltd., London Pearson Education Australia Pty. Ltd., Sydney Pearson Education Singapore, Pte., Ltd. Pearson Education North Asia Ltd., Hong Kong Pearson Education Canada, Inc., Toronto Pearson Educación de Mexico, S.A. de C.V. Pearson Education—Japan, Tokyo Pearson Education Malaysia, Pte. Ltd. Pearson Education, Inc., Upper Saddle River, New Jersey

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Preface When something can be read without effort, great effort has gone into its writing. Enrique Jardiel Poncela This edition of Digital Image Processing is a major revision of the book. As in the 1977 and 1987 editions by Gonzalez and Wintz, and the 1992 and 2002 editions by Gonzalez and Woods, this fifth-generation edition was prepared with students and instructors in mind. The principal objectives of the book continue to be to provide an introduction to basic concepts and methodologies for digital image processing, and to develop a foundation that can be used as the basis for further study and research in this field. To achieve these objectives, we focused again on material that we believe is fundamental and whose scope of application is not limited to the solution of specialized problems. The mathematical complexity of the book remains at a level well within the grasp of college seniors and first-year graduate students who have introductory preparation in mathematical analysis, vectors, matrices, probability, statistics, linear systems, and computer programming. The book Web site provides tutorials to support readers needing a review of this background material. One of the principal reasons this book has been the world leader in its field for more than 30 years is the level of attention we pay to the changing educational needs of our readers. The present edition is based on the most extensive survey we have ever conducted. The survey involved faculty, students, and independent readers of the book in 134 institutions from 32 countries. The major findings of the survey indicated a need for: ● ● ● ● ● ● ● ● ● ● A more comprehensive introduction early in the book to the mathematical tools used in image processing. An expanded explanation of histogram processing techniques. Stating complex algorithms in step-by-step summaries. An expanded explanation of spatial correlation and convolution. An introduction to fuzzy set theory and its application to image processing. A revision of the material dealing with the frequency domain, starting with basic principles and showing how the discrete Fourier transform follows from data sampling. Coverage of computed tomography (CT). Clarification of basic concepts in the wavelets chapter. A revision of the data compression chapter to include more video compression techniques, updated standards, and watermarking. Expansion of the chapter on morphology to include morphological reconstruction and a revision of gray-scale morphology. xv

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xvi ■ Preface ● ● ● Expansion of the coverage on image segmentation to include more advanced edge detection techniques such as Canny’s algorithm, and a more comprehensive treatment of image thresholding. An update of the chapter dealing with image representation and description. Streamlining the material dealing with structural object recognition. The new and reorganized material that resulted in the present edition is our attempt at providing a reasonable degree of balance between rigor, clarity of presentation, and the findings of the market survey, while at the same time keeping the length of the book at a manageable level. The major changes in this edition of the book are as follows. Chapter 1: A few figures were updated and part of the text was rewritten to correspond to changes in later chapters. Chapter 2: Approximately 50% of this chapter was revised to include new images and clearer explanations. Major revisions include a new section on image interpolation and a comprehensive new section summarizing the principal mathematical tools used in the book. Instead of presenting “dry” mathematical concepts one after the other, however, we took this opportunity to bring into Chapter 2 a number of image processing applications that were scattered throughout the book. For example, image averaging and image subtraction were moved to this chapter to illustrate arithmetic operations. This follows a trend we began in the second edition of the book to move as many applications as possible early in the discussion not only as illustrations, but also as motivation for students. After finishing the newly organized Chapter 2, a reader will have a basic understanding of how digital images are manipulated and processed. This is a solid platform upon which the rest of the book is built. Chapter 3: Major revisions of this chapter include a detailed discussion of spatial correlation and convolution, and their application to image filtering using spatial masks. We also found a consistent theme in the market survey asking for numerical examples to illustrate histogram equalization and specification, so we added several such examples to illustrate the mechanics of these processing tools. Coverage of fuzzy sets and their application to image processing was also requested frequently in the survey. We included in this chapter a new section on the foundation of fuzzy set theory, and its application to intensity transformations and spatial filtering, two of the principal uses of this theory in image processing. Chapter 4: The topic we heard most about in comments and suggestions during the past four years dealt with the changes we made in Chapter 4 from the first to the second edition. Our objective in making those changes was to simplify the presentation of the Fourier transform and the frequency domain. Evidently, we went too far, and numerous users of the book complained that the new material was too superficial. We corrected that problem in the present edition. The material now begins with the Fourier transform of one continuous variable and proceeds to derive the discrete Fourier transform starting with basic concepts of sampling and convolution. A byproduct of the flow of this

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■ Preface material is an intuitive derivation of the sampling theorem and its implications. The 1-D material is then extended to 2-D, where we give a number of examples to illustrate the effects of sampling on digital images, including aliasing and moiré patterns. The 2-D discrete Fourier transform is then illustrated and a number of important properties are derived and summarized. These concepts are then used as the basis for filtering in the frequency domain. Finally, we discuss implementation issues such as transform decomposition and the derivation of a fast Fourier transform algorithm. At the end of this chapter, the reader will have progressed from sampling of 1-D functions through a clear derivation of the foundation of the discrete Fourier transform and some of its most important uses in digital image processing. Chapter 5: The major revision in this chapter was the addition of a section dealing with image reconstruction from projections, with a focus on computed tomography (CT). Coverage of CT starts with an intuitive example of the underlying principles of image reconstruction from projections and the various imaging modalities used in practice. We then derive the Radon transform and the Fourier slice theorem and use them as the basis for formulating the concept of filtered backprojections. Both parallel- and fan-beam reconstruction are discussed and illustrated using several examples. Inclusion of this material was long overdue and represents an important addition to the book. Chapter 6: Revisions to this chapter were limited to clarifications and a few corrections in notation. No new concepts were added. Chapter 7: We received numerous comments regarding the fact that the transition from previous chapters into wavelets was proving difficult for beginners. Several of the foundation sections were rewritten in an effort to make the material clearer. Chapter 8: This chapter was rewritten completely to bring it up to date. New coding techniques, expanded coverage of video, a revision of the section on standards, and an introduction to image watermarking are among the major changes. The new organization will make it easier for beginning students to follow the material. Chapter 9: The major changes in this chapter are the inclusion of a new section on morphological reconstruction and a complete revision of the section on gray-scale morphology. The inclusion of morphological reconstruction for both binary and gray-scale images made it possible to develop more complex and useful morphological algorithms than before. Chapter 10: This chapter also underwent a major revision. The organization is as before, but the new material includes greater emphasis on basic principles as well as discussion of more advanced segmentation techniques. Edge models are discussed and illustrated in more detail, as are properties of the gradient. The Marr-Hildreth and Canny edge detectors are included to illustrate more advanced edge detection techniques. The section on thresholding was rewritten also to include Otsu’s method, an optimum thresholding technique whose popularity has increased significantly over the past few years. We introduced this approach in favor of optimum thresholding based on the Bayes classification rule, not only because it is easier to understand and implement, but also xvii

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