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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 andtechniques related to three-dimensional (stereo) vision and motion. The emphasis is on usinggeometry to solve problems in stereo and motion, with examples from navigation and objectrecognition.Faugeras takes up such important problems in computer vision as projective geometry,camera calibration, edge detection, stereo vision (with many examples on real images), differentkinds of representations and transformations (especially 3-D rotations), uncertainty and methods ofaddressing it, and object representation and recognition. His theoretical account is illustratedwith the results of actual working programs.Three-Dimensional Computer Vision proposes solutions toproblems arising from a specific robotics scenario in which a system must perceive and act. Movingabout an unknown environment, the system has to avoid static and mobile obstacles, build models ofobjects and places in order to be able to recognize and locate them, and characterize its own motionand that of moving objects, by providing descriptions of the corresponding three-dimensionalmotions. The ideas generated, however, can be used indifferent settings, resulting in a general bookon computer vision that reveals the fascinating relationship of three-dimensional geometry and theimaging process.Olivier Faugeras is Research Director of the Computer Vision and Robotics Laboratoryat INRIA Sophia-Antipolis and a Professor of Applied Mathematics at the Ecole Polytechnique inParis.
The purpose of computer vision is to make computers capable of understanding environments from visual information. Computer vision has been an interesting theme in the field of artificial intelligence. It involves a variety of intelligent information processing: both pattern processing for extraction of meaningful symbols from visual information and symbol processing for determining what the symbols represent. The term "3D computer vision" is used if visual information has to be interpreted as three-dimensional scenes. 3D computer vision is more challenging because objects are seen from limited directions and some objects are occluded by others. In 1980, the author wrote a book "Computer Vision" in Japanese to introduce an interesting new approach to visual information processing developed so far. Since then computer vision has made remarkable progress: various rangefinders have become available, new methods have been developed to obtain 3D informa tion, knowledge representation frameworks have been proposed, geometric models which were developed in CAD/CAM have been used for computer vision, and so on. The progress in computer vision technology has made it possible to understand more complex 3 D scenes. There is an increasing demand for 3D computer vision. In factories, for example, automatic assembly and inspection can be realized with fewer con straints than conventional ones which employ two-dimensional computer vision.
This computer vision textbook describes the reconstruction of object surfaces and the analysis of distances between camera and objects. Main topics are static and dynamic stereo analysis, shape from shading, photometric stereo analysis, and structured illumination. The selected procedures, e.g., complex algorithms as Tsai calibration, Frankot-Chellapa depth map generation, or Lee-Rosenfield shape from shading, are discussed at a detailed level such that implementations can follow the given descriptions. Fundamentals are given for these application oriented approaches with respect to camera modeling and calibration, to geometric surface modeling, and to surface reflectance models. New research and laboratory results in shape reconstruction and depth analysis, e.g., based on color images have been included. The text is suitable for graduate courses in computer science, in several engineering disciplines, or in applied mathematics. Theoretical and applied excercises accompany each chapter.
This work provides an introduction to the foundations of three-dimensional c- puter vision and describes recent contributions to the ?eld, which are of methodical and application-speci?c nature. Each chapter of this work provides an extensive overview of the corresponding state of the art, into which a detailed description of new methods or evaluation results in application-speci?c systems is embedded. Geometric approaches to three-dimensional scene reconstruction (cf. Chapter 1) are primarily based on the concept of bundle adjustment, which has been developed more than 100 years ago in the domain of photogrammetry. The three-dimensional scene structure and the intrinsic and extrinsic camera parameters are determined such that the Euclidean backprojection error in the image plane is minimised, u- ally relying on a nonlinear optimisation procedure. In the ?eld of computer vision, an alternative framework based on projective geometry has emerged during the last two decades, which allows to use linear algebra techniques for three-dimensional scene reconstructionand camera calibration purposes. With special emphasis on the problems of stereo image analysis and camera calibration, these fairly different - proaches are related to each other in the presented work, and their advantages and drawbacks are stated. In this context, various state-of-the-artcamera calibration and self-calibration methods as well as recent contributions towards automated camera calibration systems are described. An overview of classical and new feature-based, correlation-based, dense, and spatio-temporal methods for establishing point c- respondences between pairs of stereo images is given.
Machine Vision for Three-Dimensional Scenes contains the proceedings of the workshop "Machine Vision - Acquiring and Interpreting the 3D Scene" sponsored by the Center for Computer Aids for Industrial Productivity (CAIP) at Rutgers University and held in April 1989 in New Brunswick, New Jersey. The papers explore the applications of machine vision in image acquisition and 3D scene interpretation and cover topics such as segmentation of multi-sensor images; the placement of sensors to minimize occlusion; and the use of light striping to obtain range data. Comprised of 14 chapters, this book opens with a discussion on 3D object recognition and the problems that arise when dealing with large object databases, along with solutions to these problems. The reader is then introduced to the free-form surface matching problem and object recognition by constrained search. The following chapters address the problem of machine vision inspection, paying particular attention to the use of eye tracking to train a vision system; images of 3D scenes and the attendant problems of image understanding; the problem of object motion; and real-time range mapping. The final chapter assesses the relationship between the developing machine vision technology and the marketplace. This monograph will be of interest to practitioners in the fields of computer science and applied mathematics.
An introduction to color in three-dimensional image processing and the emerging area of multi-spectral image processing The importance of color information in digital image processing is greater than ever. However, the transition from scalar to vector-valued image functions has not yet been generally covered in most textbooks. Now, Digital Color Image Processing fills this pressing need with a detailed introduction to this important topic. In four comprehensive sections, this book covers: The fundamentals and requirements for color image processing from a vector-valued viewpoint Techniques for preprocessing color images Three-dimensional scene analysis using color information, as well as the emerging area of multi-spectral imaging Applications of color image processing, presented via the examination of two case studies In addition to introducing readers to important new technologies in the field, Digital Color Image Processing also contains novel topics such as: techniques for improving three-dimensional reconstruction, three-dimensional computer vision, and emerging areas of safety and security applications in luggage inspection and video surveillance of high-security facilities. Complete with full-color illustrations and two applications chapters, Digital Color Image Processing is the only book that covers the breadth of the subject under one convenient cover. It is written at a level that is accessible for first- and second-year graduate students in electrical and computer engineering and computer science courses, and that is also appropriate for researchers who wish to extend their knowledge in the area of color image processing.

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