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Endowing machines with a sense of vision has been a dream of scientists and engineers 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
undergraduates 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.