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Computer Vision, by Linda G. Shapiro, George C. Stockman
Ebook Computer Vision, by Linda G. Shapiro, George C. Stockman
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Using a progressive intuitive/mathematical approach, this introduction to computer vision provides necessary theory and examples for practitioners who work in fields where significant information must be extracted automatically from images-- including those interested in multimedia, art and design, geographic information systems, and image databases, in addition to the traditional areas of automation, image science, medical imaging, remote sensing and computer cartography. The book provides a basic set of fundamental concepts, (representations of image information, extraction of 3D scene information from 2D images, etc.) algorithms for analyzing images, and discusses some of the exciting evolving application areas of computer vision. The approach is language and software independent, and includes two significant commercial case studies. Imaging and Image Representation. Binary Image Analysis. Pattern Recognition Concepts. Filtering and Enhancing Images. Color and Shading. Texture. Content-Based Image Retrieval. Motion from 2D Image Sequences. Image Segmentation. Matching in 2D. Perceiving 3D from 2D Images. 3D Sensing and Object Pose Computation. 3D Models and Matching. Virtual Reality. Case Studies. For practitioners in any field where information must be extracted automatically from images.
- Sales Rank: #161538 in Books
- Published on: 2001-02-02
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x 1.30" w x 7.00" l, 2.25 pounds
- Binding: Paperback
- 608 pages
From the Inside Flap
Preface
This book is intended as an introduction to computer vision for a broad audience. It provides necessary theory and examples for students and practitioners who will work in fields where significant information must be extracted automatically from images. The book should be a useful resource for professionals, a text for both undergraduate and beginning graduate courses, and a resource for enrichment of college or even high school projects. Our goals were to provide a basic set of fundamental concepts and algorithms and also discuss some of the exciting evolving application areas. This book is unique in that it contains chapters on image databases (Chapter 8) and on virtual and augmented reality (Chapter 15), two exciting evolving application areas. A final chapter (Chapter 16) gives a complete view of real-world systems that use computer vision.
Due to recent progress in the computer field, economical and flexible use of computer images is now pervasive. Computing. with images is no longer just for the realm of the sciences, but also for the arts and social sciences and even for hobbyists. The book should serve an established and growing audience including those interested in multimedia; art and design; geographic information systems; and image databases, in addition to the traditional areas of automation, image science, medical imaging, remote sensing, and computer cartography.
A broad purpose at first seems impossible to achieve. However, there are other kinds of texts that already do this in other areas—calculus, physics, and general computing. We hope we have made at least a good beginning—we wanted a book that would be useful in the classroom and also to the independent reader. We find the chosen topics interesting and sometimes exciting, and hope that they are accessible to a large audience. It is assumed that use of the text in a graduate, or even senior level, computer vision course would be supplemented by papers from the archival literature. Coverage is not intended to be comprehensive; only a modest set of papers are cited at the end of each chapter.
The early chapters begin at an intuitive level and progress towards mathematical models with the goal of intuitive understanding before formal characterization. Sections marked by an asterisk (*) are more mathematical or more advanced and need not be covered in a less technical course. To strengthen the intuitive approach, we have stayed with the processing of iconic imagery for the first eleven chapters and have delayed 3D computer vision until the later chapters, but it should be easy for experienced instructors to resequence them to fit a particular course or teaching style. There are many viable applications that are entirely 2D, and many concepts and algorithms are more simply taught in their 2D form. We provide some basics of pattern recognition in Chapter 4, so that students can consider complete recognition systems before the full coverage of image features and matching. A reader should have a good idea of 2D image processing applications after Chapter 4; Chapters 5, 6, and 7 add in gray-tone, color, and texture features. Chapter 8 treats image databases, a popular recent topic. Although some colleagues advised us to place this material near the end of the book, our goal of positioning it early in the chapter sequence is to reinforce the concepts of the prior chapters and to provide material that can lead to an excellent half-term project. Segmentation and matching are treated in their 2D forms in Chapters 10 and 11, so that the basic concepts are presented in a simple form, without introducing the complexities of 3D transformations.
Characteristics of the 3D world are briefly introduced in Chapter 2 and then are studied in much more detail in Chapter 12. Chapter 12 surveys qualitatively many aspects of how a 3D world can be perceived from 2D images: It concludes with quantitative models of stereo and study of the thin lens equation for depth-from-focus and resolving power. The transition to 3D computer vision is made in Chapter 13: The authors have found from their own teaching that the difficulty increases abruptly for students at this point. The use of matrices to model homogeneous transformations are included within the chapter rather than in appendices; the 3D versions are extensions of the simpler 2D versions given in Chapter 11. Least-squares fitting, introduced in a simple 2D context in Chapter 11, is also extended in Chapter 13. Non-linear optimization is introduced in a simple P3P context and then used for camera calibration including the modeling of radial distortion in a lens. Chapter 14 treats 3D models and the matching of models to 3D sensed data: it is of mixed difficulty. Chapter 15 discusses applications in virtual and augmented (mixed) reality and the role of computer vision techniques. Programming Language Issue
The book does not rely on any programming language, but uses a generic algorithmic notation. Commitment to a particular language is unnecessary and would be the wrong language for many readers. Students who are programmers should have little trouble implementing the algorithms, as our own students have shown. Examples will eventually be provided on the World Wide Web when appropriate and available, primarily so students can quickly experiment, secondarily so that they can study some sample code.
Several tools and libraries are available to instructors and students; for example, Khoros, NIH-Image, XView, gimp, MATLAB, etc. There are also packages that can be purchased from companies that make machine vision hardware. The authors have decided not to base the text on any specific software because, first, most readers would be using something else, and second, it would be counterproductive to bury the essence of the image operations within the complex framework of data structures and methods needed in an industrial strength system. Having first studied principles in an environment with few variables, the reader will then be better able to successfully choose and use an industrial system. Ways to Use the Text
The book material can be selected, and sometimes sequenced, in different ways according to the goal of the course and interests of the instructor and students.
Chapter 3, with brief summary of Chapter 2
A minimum usage would be 1-3 lectures in a data structures and algorithms course. Chapter 3, with some background from Chapter 2 contains motivational applications and programming exercises on 2D arrays, depth-first search, and the union-find data structure for sets. Chapters 1, 2, and 3, and optionally some of Chapters 4, 5, and 6
This could serve as an enrichment unit of 1 to 3 weeks for high school or lower division undergrads. The objective could be as simple as a term paper or as complex as group work on a program to, say, create a 2D parts recognition system based on connected components and prototype matching of feature vectors. Much of Chapters 1-11
This would be a survey of 2D material for an elective course for students in geography, natural resources or microbiology, for example, provided that many of the optional sections are passed over. If most sections of Chapters 1-11 are covered, this would constitute a semester undergraduate course in image processing and analysis with an introduction to computer vision. Most of the text
This would constitute a semester course in computer vision for the senior or first year graduate student level. There is more material in the book than can be covered well in one semester. Some sections will have to be ignored or surveyed and the reader should not be expected to be able to work homework problems in all sections. For the quarter system, Chapters 1-4, 6-12, and 14 make a good introduction to computer vision for undergraduates. For a one quarter graduate course, Chapters 1-4 can be minimally covered with the emphasis on Chapters 6-14 and a brief coverage of Chapter 15. For any graduate level course, it is expected that some papers from the current literature would also be covered.
We are grateful to our many colleagues, teachers, and students with whom we have shared our interests. They have contributed much to our growing field and shared their work and excitement. Many have generously supported this book with encouragement and with contributions of ideas, figures, and algorithms. Specific citations are given throughout the book. With regret we have left out some important contributions—a text can only be so large. The several reviewers and many colleagues who have given us feedback have significantly improved our work. In particular, for careful editing, we are indebted to Mohammad Ghavamzadeh, Nick Dutta, Kevin Bowyer, Adam Clark, Yu-Yu Chou, Habib Abi-Racked, and Valentin Razmov. We take responsibility for any errors remaining in the book and for providing corrections in the future.
This book was four years in the making. We are indebted to Paul Becker of Addison Wesley-Longman for much guidance in getting the project going and to Tom Robbins of Prentice Hall for finishing it off. We thank Cathy Davison and Lorraine Evans for their persistence in helping to resolve the many cases where permissions needed to be tracked down. We are grateful to Rose Rummel-Eury and Chanda Wakefield of ICC for meticulous editing of our notation and English, and for pushing the schedule. Creating the book was not light work and it certainly helped to have a team with both skill and humor.
Linda Shapiro
shapiro@cs.washington
George Stockman
stockman@cse.msu
From the Back Cover
Scientists and science fiction writers have long been fascinated by the possibility of building intelligent machines and the capability of understanding the visual world is a prerequisite for such a machine. This book speaks to the notable research progress being conducted and brings together the important problem areas where computer vision is already providing solutions. Due to recent progress in the computer field, economical and flexible use of computer images is pervasive. Computing with images is no longer just for the realm of the sciences, but also for the arts and social sciences and even for hobbyists. This book should serve an established and growing audience including those interested in multimedia, art and design, geographic information systems, and image databases, in addition to the traditional areas of automation, image science, medical imaging, remote sensing, and computer cartography.
Computer Vision presents the necessary theory and techniques for students and practitioners who will work in fields where significant information must be extracted automatically from images. It will be a useful resource automatically from images. It will be a useful resource book for professionals and a core text for both undergraduate and beginning graduate computer vision and imaging courses.
Features
- Topics include image databases an virtual and augmented reality in addition to classical topics.
- Offers a complete view of two real-world systems that use computer vision.
- Contains applications from industry, medicine, land use, multimedia, and computer graphics.
- Includes over 250 exercises and programming projects, 48 separately defined algorithms, and 360 figures.
- The companion website features include image archive, sample
About the Author
LINDA G. SHAPIRO is Professor of Computer Science and Engineering and Professor of Electrical Engineering at the University of Washington. She earned a bachelor's degree in mathematics from the University of Illinois in 1970 and master's and Ph.D. degrees in computer science from the University of Iowa in 1972 and 1974, respectively. She taught at Kansas State University and at Virginia Polytechnic Institute and State University and served as Director of Intelligent Systems at Machine Vision International before joining the University of Washington in 1986. Professor Shapiro is past editor-in-chief of the journal Image Understanding and is a member of the editorial boards of Computer Vision and Image Understanding and of Pattern Recognition. She has served on the program committees of many computer vision workshops and conference and is co-author of the text, Computer and Robot Vision with Robert M. Haralick. She was elected a Fellow of the IEEE in 1995, and a Fellow of the International Association for Pattern Recognition in 2000.
GEORGE C. STOCKMAN received the B.S. degree in Mathematics-Education from East Stroudsburg State University in 1966, the MAT degree from Harvard in 1967, the M.S. degree in computer science from Penn State in 1971, and the Ph.D. degree in computer science from The University of Maryland in 1977. Currently he is a Professor of Computer Science and Engineering at Michigan State University, where he joined the faculty in 1982. From 1974 to 1982, he worked as a Research Scientist for LNK Corporation on problems in image analysis and computer cartography. At MSU he teaches programming and data structures as well as computer vision and computer graphics. Professor Stockman has been active in many activities of the IEEE, including workshops on the teaching of computing with images.
Most helpful customer reviews
38 of 39 people found the following review helpful.
Excellent introduction guide
By A Customer
The book presents a nice complement to Image Processing, Analysis and Machine Vision (Image Processing, Analysis, and Machine Vision, 2nd ed., M. Sonka, V. Hlavac, and R. Boyle, 1998, IPAMV). As the difference in names implies, Computer Vision is not appropriate as an image processing textbook. It contains sufficient information on image processing to implement computer vision algorithms, but the focus of the book is on image analysis and high-level vision. The result is that the combination of IPAMV and Computer Vision cover the spectrum from intensive image processing and manipulation to high level analysis, object recognition and content-based image retrieval.
Computer Vision contains sixteen chapters that fall into roughly four categories: overview, 2-D CV topics, 3D CV topics, and special CV topics. Since it was written with the intent of reaching a broader audience than IPAMV, this book is appropriate as a primary text or reference for a wider variety of courses. For example, it would be appropriate for courses ranging from an introduction to imaging for non-scientists to a sophomore-junior elective to a first-year graduate seminar.
The overview chapters (chapters 1-4) include a summary of problems in CV, imaging and image representations, simple binary image analysis and a survey of pattern recognition concepts. The 2-D processing topics (chapters 3, 5-7, and 11) include thresholding and binary image analysis, filtering and enhancement, edge detection, Fourier Transforms, color, texture, segmentation, and 2-D matching and pose calculation. The 3-D computer vision topics (chapters 9-10, and 12-14) include motion detection and analysis, range image analysis, stereo, calibration, intrinsic image analysis and line labeling, shape from X, and camera models. The special topics (chapters 6-8, 15-16) include color and shading, texture, content-based retrieval, virtual reality, and a set of case studies of CV systems. Different combinations of these are appropriate for different types of courses.
In comparison with other texts, the coverage of color and shading in Computer Vision is the best available without consulting a color reference such as Fairchild's Color Appearance Models (described below). However, it still does not contain adequate coverage of physical models of reflection or color appearance. The texture chapter is comparable to Sonka et. al., and the CBIR and VR chapters are unique. It is these latter two areas that give Computer Vision a nice high-level flavor and provides a reference for these growing areas of CV.
Like IPAMV, Computer Vision contains a large number of example images, diagrams, and algorithms. The writing is clear and the mathematics--when it is necessary to present it--is complete and accessible. Since the book is designed with multiple audiences in mind, the heavy mathematical sections are flagged and the book can be used effectively with or without them.
Of particular interest to CV practitioners and students dealing with issues of calibration, chapter 13 contains a nice description of Roger Tsai's camera calibration algorithm, complete with an example. Note that Trucco and Verri (see below) also cover Tsai's calibration algorithm.
Overall, the choice between Computer Vision and IPAMV should be based on personal preference, the focus of your course, and the background of your students. IPAMV will be more accessible to engineers and contains more in-depth coverage of image processing techniques. Computer Vision is more accessible to computer scientists and covers a number of higher-level aspects of CV that are either not covered or briefly covered in IPAMV. In a number of areas--texture, stereo, motion, calibration, and segmentation--the two books are quite similar and the differences are mainly in style and emphasis.
33 of 35 people found the following review helpful.
Great book but paperback version is a disappointment
By Jason
Shapiro's "Computer Vision" is an excellent book for someone looking for an introductory text in the field. The book is well structured and introduces fundamental concepts first, then uses these concepts to build on advanced approaches. The book assumes some knowledge of mathematics in linear algebra, calculus, and set theory, but does a good job of introducing the concepts before jumping into the math. Compared to other vision books this book is less focused on the math and more focused on conveying the concepts. Math intensive sections are noted up front in the table of contents.
The reason I rate this book 3 out of 5 stars is that the paperback is entirely in black and white. The original hardcover contained both color images and color plates. When these images are converted to B&W it defeats the purpose of having them as the reader cannot distinguish some of the effects occurring in the image which Shapiro is discussing (especially in sections discussing color image processing!). It is very disappointing that the publisher opted to go this route. If possible I recommend obtaining a hardcover which I give 5 out of 5 stars.
16 of 17 people found the following review helpful.
Good presentation of both beginning and advanced material
By calvinnme
Of the several computer vision textbooks that I haved owned and read, this book provides the best combination of introductory techniques with more advanced material in a very readable style.
The first two chapters are a very conversational overview of computer vision and image representation, but don't let this fool you. Starting in chapter three, the book becomes concise in presentation and in numerical examples. The authors starts out with the basics of binary image analysis which includes a very good discussion of image morphology. However, this is not an image processing book, so you should already be familiar with image processing on the same level as what is presented in Gonzales & Wood's "Digital Image Processing", which is my personal favorite among the various image processing texts. Next pattern recognition basics are discussed, including a section on neural networks that was clearer than anything I gleaned from Haykin's classic text on the subject. Next, the author moves into the realm of gray scale images by discussing the filtering and enhancing of images, which is similar to material in many image processing books. The basics of computer vision conclude with chapters on color, shading, and texture. Next, the book jumps into more advanced material that builds on the introductory material. For example, there are chapters on content-based image retrieval, a subject on which the author Linda Shapiro is conducting research at the University of Washington, and also on computing motion from 2D image sequences. Finally, the book tackles some 3D computer vision issues such as perceiving 3D from 2D images, object pose computation, and 3D models and matching using image "snakes". There are algorithms presented in pseudocode throughout this book, along with supporting mathematics, so the reader should have a good understanding of matrix algebra as well as calculus to really get the most from this book. The algorithms are concisely represented, and I had no trouble coding up a content-based image retrieval program based solely on the contents of this book. The pattern recognition chapter lacks a few details, and it might be helpful if the reader had a copy of Tom Mitchell's "Machine Learning", which parallels nicely with the pattern recognition chapter of Shapiro's book and is both complete and concise.
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