Three-Dimensional Computer Vision
This monograph by one of the world's leading vision researchers provides a thorough, mathematically rigorous exposition of a broad and vital area in computer vision: the problems and techniques related to three-dimensional (stereo) vision and motion. The emphasis is on using geometry to solve problems in stereo and motion, with examples from navigation and object recognition.
Faugeras takes up such important problems in computer vision as projective geometry, camera calibration, edge detection, stereo vision (with many examples on real images), different kinds of representations and transformations (especially 3-D rotations), uncertainty and methods of addressing it, and object representation and recognition. His theoretical account is illustrated with the results of actual working programs.
Three-Dimensional Computer Vision proposes solutions to problems arising from a specific robotics scenario in which a system must perceive and act. Moving about an unknown environment, the system has to avoid static and mobile obstacles, build models of objects and places in order to be able to recognize and locate them, and characterize its own motion and that of moving objects, by providing descriptions of the corresponding three-dimensional motions. The ideas generated, however, can be used indifferent settings, resulting in a general book on computer vision that reveals the fascinating relationship of three-dimensional geometry and the imaging process.
Olivier Faugeras is Research Director of the Computer Vision and Robotics Laboratory at INRIA Sophia-Antipolis and a Professor of Applied Mathematics at the Ecole Polytechnique in Paris.
About the Author
Olivier Faugeras is Research Director and head of a computer vision group at INRIA and Adjunct Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. He is the author of Three-Dimensional Computer Vision (MIT Press, 1993).
"A magnificant tour de force. Three-Dimensional Computer Vision deals with an extremely broad and important chunk of computer vision and covers the area with excellent breadth. It provides examples of the described techniques being applied to real images, and it is built on the kind of solid mathematical underpinnings that are essential if the field is to move from the 'black art' stage to a real science. Anyone who claims to be serious about research in this area absolutely must be aware of this work."
—W. Eric L. Grimson, AI Laboratory, M.I.T.