Ebook: An Invitation to 3-D Vision: From Images to Geometric Models

Endowing machines with a sense of vision has been a dream of scientists and engineers alike for over half a century. Only in the past decade, however, has the geometry of vision been understood to the point where this dream becomes attainable, thanks also to the remarkable progress in imaging and computing hardware.

This book addresses a central problem in computer vision -- how to recover 3-D structure and motion from a collection of 2-D images -- using techniques drawn mainly from linear algebra and matrix theory. The stress is on developing a unified framework for studying the geometry of multiple images of a 3-D scene and reconstructing geometric models from those images. The book also covers relevant aspects of image formation, basic image processing, and feature extraction. The authors bridge the gap between theory and practice by providing step-by-step instructions for the implementation of working vision algorithms and systems.

Written primarily as a textbook, the aim of this book is to give senior undergraduate and beginning graduate students in computer vision, robotics, and computer graphics a solid theoretical and algorithmic foundation for future research in this burgeoning field. It is entirely self-contained with necessary background material covered in the beginning chapters and appendices, and plenty of exercises, examples, and illustrations given throughout the text.

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Endowing machines with a sense of vision has been a dream of scientists and engineers alike for over half a century. Only in the past decade, however, has the geometry of vision been understood to the point where this dream becomes attainable, thanks also to the remarkable progress in imaging and computing hardware.

This book addresses a central problem in computer vision -- how to recover 3-D structure and motion from a collection of 2-D images -- using techniques drawn mainly from linear algebra and matrix theory. The stress is on developing a unified framework for studying the geometry of multiple images of a 3-D scene and reconstructing geometric models from those images. The book also covers relevant aspects of image formation, basic image processing, and feature extraction. The authors bridge the gap between theory and practice by providing step-by-step instructions for the implementation of working vision algorithms and systems.

Written primarily as a textbook, the aim of this book is to give senior undergraduate and beginning graduate students in computer vision, robotics, and computer graphics a solid theoretical and algorithmic foundation for future research in this burgeoning field. It is entirely self-contained with necessary background material covered in the beginning chapters and appendices, and plenty of exercises, examples, and illustrations given throughout the text.

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Endowing machines with a sense of vision has been a dream of scientists and engineers alike for over half a century. Only in the past decade, however, has the geometry of vision been understood to the point where this dream becomes attainable, thanks also to the remarkable progress in imaging and computing hardware.

This book addresses a central problem in computer vision -- how to recover 3-D structure and motion from a collection of 2-D images -- using techniques drawn mainly from linear algebra and matrix theory. The stress is on developing a unified framework for studying the geometry of multiple images of a 3-D scene and reconstructing geometric models from those images. The book also covers relevant aspects of image formation, basic image processing, and feature extraction. The authors bridge the gap between theory and practice by providing step-by-step instructions for the implementation of working vision algorithms and systems.

Written primarily as a textbook, the aim of this book is to give senior undergraduate and beginning graduate students in computer vision, robotics, and computer graphics a solid theoretical and algorithmic foundation for future research in this burgeoning field. It is entirely self-contained with necessary background material covered in the beginning chapters and appendices, and plenty of exercises, examples, and illustrations given throughout the text.

Content:
Front Matter....Pages i-xx
Introduction....Pages 1-12
Front Matter....Pages 13-13
Representation of a Three-Dimensional Moving Scene....Pages 15-43
Image Formation....Pages 44-74
Image Primitives and Correspondence....Pages 75-106
Front Matter....Pages 107-107
Reconstruction from Two Calibrated Views....Pages 109-170
Reconstruction from Two Uncalibrated Views....Pages 171-227
Estimation of Multiple Motions from Two Views....Pages 228-260
Front Matter....Pages 261-261
Multiple-View Geometry of Points and Lines....Pages 263-309
Extension to General Incidence Relations....Pages 310-337
Geometry and Reconstruction from Symmetry....Pages 338-372
Front Matter....Pages 373-373
Step-by-Step Building of a 3-D Model from Images....Pages 375-411
Visual Feedback....Pages 412-438
Back Matter....Pages 439-527

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Endowing machines with a sense of vision has been a dream of scientists and engineers alike for over half a century. Only in the past decade, however, has the geometry of vision been understood to the point where this dream becomes attainable, thanks also to the remarkable progress in imaging and computing hardware.

This book addresses a central problem in computer vision -- how to recover 3-D structure and motion from a collection of 2-D images -- using techniques drawn mainly from linear algebra and matrix theory. The stress is on developing a unified framework for studying the geometry of multiple images of a 3-D scene and reconstructing geometric models from those images. The book also covers relevant aspects of image formation, basic image processing, and feature extraction. The authors bridge the gap between theory and practice by providing step-by-step instructions for the implementation of working vision algorithms and systems.

Written primarily as a textbook, the aim of this book is to give senior undergraduate and beginning graduate students in computer vision, robotics, and computer graphics a solid theoretical and algorithmic foundation for future research in this burgeoning field. It is entirely self-contained with necessary background material covered in the beginning chapters and appendices, and plenty of exercises, examples, and illustrations given throughout the text.

Content:
Front Matter....Pages i-xx
Introduction....Pages 1-12
Front Matter....Pages 13-13
Representation of a Three-Dimensional Moving Scene....Pages 15-43
Image Formation....Pages 44-74
Image Primitives and Correspondence....Pages 75-106
Front Matter....Pages 107-107
Reconstruction from Two Calibrated Views....Pages 109-170
Reconstruction from Two Uncalibrated Views....Pages 171-227
Estimation of Multiple Motions from Two Views....Pages 228-260
Front Matter....Pages 261-261
Multiple-View Geometry of Points and Lines....Pages 263-309
Extension to General Incidence Relations....Pages 310-337
Geometry and Reconstruction from Symmetry....Pages 338-372
Front Matter....Pages 373-373
Step-by-Step Building of a 3-D Model from Images....Pages 375-411
Visual Feedback....Pages 412-438
Back Matter....Pages 439-527
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