Computer Vision: A Modern Approach / Edition 1

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Overview

Computer Vision:
A Modern Approach

This extraordinary book gives a uniquely modern view of computer vision. Offering a general survey of the whole computer vision enterprise along with sufficient detail for readers to be able to build useful applications, this book is invaluable in providing a strategic overview of computer vision. With extensive use of probabalistic methods— topics have been selected for their importance, both practically and theoretically—the book gives the most coherent possible synthesis of current views, emphasizing techniques that have been successful in building applications. Readers engaged in computer graphics, robotics, image processing, and imaging in general will find this text an informative reference.

KEY FEATURES

  • Application Surveys—Numerous examples, including Image Based Rendering and Digital Libraries
  • Boxed Algorithms—Key algorithms broken out and illustrated in pseudo code
  • Self-Contained—No need for other references
  • Extensive, Detailed Illustrations—Examples of inputs and outputs for current methods
  • Programming Assignments—50 programming assignments and 150 exercises
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Product Details

  • ISBN-13: 9780130851987
  • Publisher: Prentice Hall Professional Technical Reference
  • Publication date: 3/1/2002
  • Edition description: Older Edition
  • Edition number: 1
  • Pages: 693
  • Product dimensions: 7.94 (w) x 10.29 (h) x 1.62 (d)

Meet the Author

David A. Forsyth received the D.Phil. degree in computer science from Oxford University. He is currently a Professor in the Computer Science Division at the University of California at Berkeley. He has co-authored over eighty technical papers on computer vision, computer graphics and machine learning and has co-edited two books.

Jean Ponce received the Ph.D. degree in Computer Science from the University of Paris Orsay. He is currently a Professor in the Department of Computer Science and the Beckman Institute at the University of Illinois at Urbana Champaign. Professor Ponce has written over a hundred conference and journal papers and co-edited two books on a range of subjects including computer vision and robotics.

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Read an Excerpt

Computer vision as a field is an intellectual frontier. Like any frontier, it is exciting and disorganised; there is often no reliable authority to appeal to—many useful ideas have no theoretical grounding, and some theories are useless in practice; developed areas are widely scattered, and often one looks completely inaccessible from the other. Nevertheless, we have attempted in this book to present a fairly orderly picture of the field.

We see computer vision—or just "vision"; apologies to those who study human or animal vision—as an enterprise that uses statistical methods to disentangle data using models constructed with the aid of geometry, physics and learning theory. Thus, in our view, vision relies on a solid understanding of cameras and of the physical process of image formation (part I of this book) to obtain simple inferences from individual pixel values (part II), combine the information available in multiple images into a coherent whole (part III), impose some order on groups of pixels to separate them from each other or infer shape information (part IV), and recognize objects using geometric information (part V) or probabilistic techniques (part VI). Computer vision has a wide variety of applications, old (e.g., mobile robot navigation, industrial inspection, and military intelligence) and new (e.g., human computer interaction, image retrieval in digital libraries, medical image analysis, and the realistic rendering of synthetic scenes in computer graphics). We discuss some of these applications in part VII. WHY STUDY VISION?

Computer vision's great trick is extracting descriptions of the world from pictures or sequences of pictures. Thisis unequivocally useful. Taking pictures is usually non-destructive and sometimes discreet. It is also easy and (now) cheap. The descriptions that users seek can differ widely between applications. For example, a technique known as structure from motion makes it possible to extract a representation of what is depicted and how the camera moved from a series of pictures. People in the entertainment industry use these techniques to build three-dimensional (3D) computer models of buildings, typically keeping the structure and throwing away the motion. These models are used where real buildings cannot be; they are set fire to, blown up, etc. Good, simple, accurate and convincing models can be built from quite small sets of photographs. People who wish to control mobile robots usually keep the motion and throw away the structure. This is because they generally know something about the area where the robot is working, but don't usually know the precise robot location in that area. They can determine it from information about how a camera bolted to the robot is moving.

There are a number of other, important applications of computer vision. One is in medical imaging: One builds software systems that can enhance imagery, or identify important phenomena or events, or visualize information obtained by imaging. Another is in inspection: One takes pictures of objects to determine whether they are within specification. A third is in interpreting satellite images, both for military purposes—a program might be required to determine what militarily interesting phenomena have occurred in a given region recently; or what damage was caused by a bombing—and for civilian purposes—what will this year's maize crop be? How much rainforest is left? A fourth is in organizing and structuring collections of pictures. We know how to search and browse text libraries (though this is a subject that still has difficult open questions) but don't really know what to do with image or video libraries.

Computer vision is at an extraordinary point in its development. The subject itself has been around since the 1960s, but it is only recently that it has been possible to build useful computer systems using ideas from computer vision. This flourishing has been driven by several trends: Computers and imaging systems have become very cheap. Not all that long ago, it took tens of thousands of dollars to get good digital color images; now it takes a few hundred, at most. Not all that long ago, a color printer was something one found in few, if any, research labs; now they are in many homes. This means it is easier to do research. It also means that there are many people with problems to which the methods of computer vision apply. For example, people would like to organize their collection of photographs, make 3D models of the world around them, and manage and edit collections of videos. Our understanding of the basic geometry and physics underlying vision and, what is more important, what to do about it, has improved significantly. We are beginning to be able to solve problems that lots of people care about, but none of the hard problems have been solved and there are plenty of easy ones that have not been solved either (to keep one intellectually fit while trying to solve hard problems). It is a great time to be studying this subject. What Is in This Book?

This book covers what we feel a computer vision professional ought to know. However, it is addressed to a wider audience. We hope that those engaged in computational geometry, computer graphics, image processing, imaging in general, and robotics will find it an informative reference. We have tried to make the book accessible to senior undergraduates or graduate students with a passing interest in vision. Each chapter covers a different part of the subject, and, as a glance at Table 1 will confirm, chapters are relatively independent. This means that one can dip into the book as well as read it from cover to cover. Generally, we have tried to make chapters run from easy material at the start to more arcane matters at the end. Each chapter has brief notes at the end, containing historical material and assorted opinions. We have tried to produce a book that describes ideas that are useful, or likely to be so in the future. We have put emphasis on understanding the basic geometry and physics of imaging, but have tried to link this with actual applications. In general, the book reflects the enormous recent influence of geometry and various forms of applied statistics on computer vision.

A reader who goes from cover to cover will hopefully be well informed, if exhausted; there is too much in this book to cover in a one-semester class. Of course, prospective (or active) computer vision professionals should read every word, do all the exercises, and report any bugs found for the second edition (of which it is probably a good idea to plan buying a copy!). While the study of computer vision does not require deep mathematics, it does require facility with a lot of different mathematical ideas. We have tried to make the book self contained, in the sense that readers with the level of mathematical sophistication of an engineering senior should be comfortable with the material of the book, and should not need to refer to other texts. We have also tried to keep the mathematics to the necessary minimum—after all, this book is about computer vision, not applied mathematics—and have chosen to insert what mathematics we have kept in the main chapter bodies instead of a separate appendix.

Generally, we have tried to reduce the interdependence between chapters, so that readers interested in particular topics can avoid wading through the whole book. It is not possible to make each chapter entirely self contained, and Table 1 indicates the dependencies between chapters. What Is Not in This Book

The computer vision literature is vast, and it was not easy to produce a book about computer vision that can be lifted by ordinary mortals. To do so, we had to cut material, ignore topics, and so on. We cut two entire chapters close to the last moment: One is an introduction to probability and inference, the other an account of methods for tracking objects with non-linear dynamics. These chapters appear on the book's web page http://www.cs.berkeley.edu/~daf/book.html.

We left out some topics because of personal taste, or because we became exhausted and stopped writing about a particular area, or because we learned about them too late to put them in, or because we had to shorten some chapter, or any of hundreds of other reasons. We have tended to omit detailed discussions of material that is mainly of historical interest, and offer instead some historical remarks at the end of each chapter. Neither of us claims to be a fluent intellectual archaeologist, meaning that ideas may have deeper histories than we have indicated. We just didn't get around to writing up deformable templates and mosaics, two topics of considerable practical importance; we will try to put them into the second edition.

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Table of Contents

Pt. I Image Formation and Image Models 1
1 Cameras 3
2 Geometric Camera Models 20
3 Geometric Camera Calibration 38
4 Radiometry-Measuring Light 55
5 Sources, Shadows, and Shading 70
6 Color 97
Pt. II Early Vision: Just One Image 133
7 Linear Filters 135
8 Edge Detection 165
9 Texture 189
Pt. III Early Vision: Multiple Images 213
10 The Geometry of Multiple Views 215
11 Stereopsis 234
12 Affine Structure from Motion 251
13 Projective Structure from Motion 274
Pt. V Mid-Level Vision 299
14 Segmentation by Clustering 301
15 Segmentation by Fitting a Model 329
16 Segmentation and Fitting Using Probabilistic Methods 354
17 Tracking with Linear Dynamic Models 373
Pt. V High-Level Vision: Geometric Methods 399
18 Model-Based Vision 401
19 Smooth Surfaces and Their Outlines 427
20 Aspect Graphs 444
21 Range Data 467
Pt. VI High-Level Vision: Probabilistic and Inferential Methods 493
22 Finding Templates Using Classifiers 495
23 Recognition by Relations Between Templates 537
24 Geometric Templates from Spatial Relations 568
Pt. VII Applications 597
25 Application: Finding in Digital Libraries 599
26 Application: Image-Based Rendering 620
Bibliography 643
Index 673
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Preface

Computer vision as a field is an intellectual frontier. Like any frontier, it is exciting and disorganised; there is often no reliable authority to appeal to—many useful ideas have no theoretical grounding, and some theories are useless in practice; developed areas are widely scattered, and often one looks completely inaccessible from the other. Nevertheless, we have attempted in this book to present a fairly orderly picture of the field.

We see computer vision—or just "vision"; apologies to those who study human or animal vision—as an enterprise that uses statistical methods to disentangle data using models constructed with the aid of geometry, physics and learning theory. Thus, in our view, vision relies on a solid understanding of cameras and of the physical process of image formation (part I of this book) to obtain simple inferences from individual pixel values (part II), combine the information available in multiple images into a coherent whole (part III), impose some order on groups of pixels to separate them from each other or infer shape information (part IV), and recognize objects using geometric information (part V) or probabilistic techniques (part VI). Computer vision has a wide variety of applications, old (e.g., mobile robot navigation, industrial inspection, and military intelligence) and new (e.g., human computer interaction, image retrieval in digital libraries, medical image analysis, and the realistic rendering of synthetic scenes in computer graphics). We discuss some of these applications in part VII.

WHY STUDY VISION?

Computer vision's great trick is extracting descriptions of the world from pictures or sequences of pictures. This is unequivocally useful. Taking pictures is usually non-destructive and sometimes discreet. It is also easy and (now) cheap. The descriptions that users seek can differ widely between applications. For example, a technique known as structure from motion makes it possible to extract a representation of what is depicted and how the camera moved from a series of pictures. People in the entertainment industry use these techniques to build three-dimensional (3D) computer models of buildings, typically keeping the structure and throwing away the motion. These models are used where real buildings cannot be; they are set fire to, blown up, etc. Good, simple, accurate and convincing models can be built from quite small sets of photographs. People who wish to control mobile robots usually keep the motion and throw away the structure. This is because they generally know something about the area where the robot is working, but don't usually know the precise robot location in that area. They can determine it from information about how a camera bolted to the robot is moving.

There are a number of other, important applications of computer vision. One is in medical imaging: One builds software systems that can enhance imagery, or identify important phenomena or events, or visualize information obtained by imaging. Another is in inspection: One takes pictures of objects to determine whether they are within specification. A third is in interpreting satellite images, both for military purposes—a program might be required to determine what militarily interesting phenomena have occurred in a given region recently; or what damage was caused by a bombing—and for civilian purposes—what will this year's maize crop be? How much rainforest is left? A fourth is in organizing and structuring collections of pictures. We know how to search and browse text libraries (though this is a subject that still has difficult open questions) but don't really know what to do with image or video libraries.

Computer vision is at an extraordinary point in its development. The subject itself has been around since the 1960s, but it is only recently that it has been possible to build useful computer systems using ideas from computer vision. This flourishing has been driven by several trends: Computers and imaging systems have become very cheap. Not all that long ago, it took tens of thousands of dollars to get good digital color images; now it takes a few hundred, at most. Not all that long ago, a color printer was something one found in few, if any, research labs; now they are in many homes. This means it is easier to do research. It also means that there are many people with problems to which the methods of computer vision apply. For example, people would like to organize their collection of photographs, make 3D models of the world around them, and manage and edit collections of videos. Our understanding of the basic geometry and physics underlying vision and, what is more important, what to do about it, has improved significantly. We are beginning to be able to solve problems that lots of people care about, but none of the hard problems have been solved and there are plenty of easy ones that have not been solved either (to keep one intellectually fit while trying to solve hard problems). It is a great time to be studying this subject.

What Is in This Book?

This book covers what we feel a computer vision professional ought to know. However, it is addressed to a wider audience. We hope that those engaged in computational geometry, computer graphics, image processing, imaging in general, and robotics will find it an informative reference. We have tried to make the book accessible to senior undergraduates or graduate students with a passing interest in vision. Each chapter covers a different part of the subject, and, as a glance at Table 1 will confirm, chapters are relatively independent. This means that one can dip into the book as well as read it from cover to cover. Generally, we have tried to make chapters run from easy material at the start to more arcane matters at the end. Each chapter has brief notes at the end, containing historical material and assorted opinions. We have tried to produce a book that describes ideas that are useful, or likely to be so in the future. We have put emphasis on understanding the basic geometry and physics of imaging, but have tried to link this with actual applications. In general, the book reflects the enormous recent influence of geometry and various forms of applied statistics on computer vision.

A reader who goes from cover to cover will hopefully be well informed, if exhausted; there is too much in this book to cover in a one-semester class. Of course, prospective (or active) computer vision professionals should read every word, do all the exercises, and report any bugs found for the second edition (of which it is probably a good idea to plan buying a copy!). While the study of computer vision does not require deep mathematics, it does require facility with a lot of different mathematical ideas. We have tried to make the book self contained, in the sense that readers with the level of mathematical sophistication of an engineering senior should be comfortable with the material of the book, and should not need to refer to other texts. We have also tried to keep the mathematics to the necessary minimum—after all, this book is about computer vision, not applied mathematics—and have chosen to insert what mathematics we have kept in the main chapter bodies instead of a separate appendix.

Generally, we have tried to reduce the interdependence between chapters, so that readers interested in particular topics can avoid wading through the whole book. It is not possible to make each chapter entirely self contained, and Table 1 indicates the dependencies between chapters.

What Is Not in This Book

The computer vision literature is vast, and it was not easy to produce a book about computer vision that can be lifted by ordinary mortals. To do so, we had to cut material, ignore topics, and so on. We cut two entire chapters close to the last moment: One is an introduction to probability and inference, the other an account of methods for tracking objects with non-linear dynamics. These chapters appear on the book's web page http://www.cs.berkeley.edu/~daf/book.html.

We left out some topics because of personal taste, or because we became exhausted and stopped writing about a particular area, or because we learned about them too late to put them in, or because we had to shorten some chapter, or any of hundreds of other reasons. We have tended to omit detailed discussions of material that is mainly of historical interest, and offer instead some historical remarks at the end of each chapter. Neither of us claims to be a fluent intellectual archaeologist, meaning that ideas may have deeper histories than we have indicated. We just didn't get around to writing up deformable templates and mosaics, two topics of considerable practical importance; we will try to put them into the second edition.

Read More Show Less

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  • Anonymous

    Posted June 13, 2003

    Best book in computer vision

    i love it. It covers most parts in computer vision in detail, especially in math!! It's great for both entrylevel and professional. A must-have for computer vision guys.

    0 out of 1 people found this review helpful.

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