http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
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.
Introduction.- Part I: Introductory Material -- Representation of a Three -- Dimensional Moving Scene -- Image Formation -- Image Primitives and Correspondence -- Part II: Geometry of two Views -- Reconstruction from Two Calibrated Views -- Estimation of Multiple Motions from Two Views -- Part III: Geometry of Multiple Views -- Multiple- view Geometry of Points and Lines -- Extension to General Incidence Relations -- Geometry and Reconstruction from Symmetry -- Part IV: Applications -- Step-by-Step building of a 3-D Model From Images -- Visual Feedback -- Part V: Appendices.-Basic facts of Linear Algebra.-Least-Variance Estimation and Filtering.-Basic Facts from Nonlinear Optimization.-References.-Glossary of Notation.-Index.
Includes bibliographical references (pages [487]-508) and index.