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.
Building a computer is much easier than most people realise. The components are relatively inexpensive, easily available and the actual assembly requires only screwdrivers, small spanners and a pair of pliers. This book will enable you to assemble your own PC with the minimum of problems.
From buying and selling PC hardware to product development and selling services, "Start Your Own Computer Business: The Unembellished Guide" offers a realistic picture of making it on your own. Bestselling author, Morris Rosenthal, has 15 years of experience in the computer business, building and repairing thousands of PCs and helping hundreds of customers. The book mixes practical advice and cautions with real-world anecdotes of successes and failures. Dollars and cents play a prominent role in the book, as Rosenthal stresses that the real challenge of succeeding in the computer business is the business part of the equation. Managing employees, inventory and scarce financial resources are covered, along with how to remain sane and when to quit. The book is illustrated with a series of original cartoons on the computer business subject.
In clear, easy-to-grasp language, the author covers many of the topics that you will need to know in order to launch and run a successful business venture.
The primary focus of this book is on techniques for segmentation of visual data. By "visual data," we mean data derived from a single image or from a sequence of images. By "segmentation" we mean breaking the visual data into meaningful parts or segments. However, in general, we do not mean "any old data": but data fundamental to the operation of robotic devices such as the range to and motion of objects in a scene. Having said that, much of what is covered in this book is far more general: The above merely describes our driving interests. The central emphasis of this book is that segmentation involves model fitting. We believe this to be true either implicitly (as a conscious or sub conscious guiding principle of those who develop various approaches) or explicitly. What makes model-fitting in computer vision especially hard? There are a number of factors involved in answering this question. The amount of data involved is very large. The number of segments and types (models) are not known in advance (and can sometimes rapidly change over time). The sensors we have involve the introduction of noise. Usually, we require fast ("real-time" or near real-time) computation of solutions independent of any human intervention/supervision. Chapter 1 summarizes many of the attempts of computer vision researchers to solve the problem of segmenta tion in these difficult circumstances."
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