The state-of-the art in computer vision: theory, applications, and programming
Whether you're a working engineer, developer, researcher, or student, this is your single authoritative source for today's key computer vision innovations. Gerard Medioni and Sing Bing Kang present advances in computer vision such as camera calibration, multi-view geometry, and face detection, and introduce important new topics such as vision for special effects and the tensor voting framework. They begin with the fundamentals, cover select applications in detail, and introduce two popular approaches to computer vision programming.
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GÉRARD MEDIONI chairs the Computer Science Department and is Professor at the Institute for Robotics and Intelligent Systems at the University of Southern California. His research interests include designing and implementing very reliable vision systems to accomplish difficult tasks and establishing bridges between computer vision and computer graphics. SING BING KANG is a member of the Interactive Visual Media Group at Microsoft Research, where he specializes in vision-based modeling. He recently co-edited Panoramic Vision: Sensors, Theory, and Applications, and has served on the technical committees of three major computer vision conferences. He holds 12 US patents.
One of the major changes instituted at the 2001 Conference on Computer Vision and Pattern Recognition (CVPR) in Kauai, Hawaii, was the replacement of the traditional tutorial sessions with a set of short courses. The topics of these short courses were carefully chosen to reflect the diversity in computer vision and represent very promising areas. The response was a very pleasant surprise, with sometimes more than 200 people attending a single short course. This overwhelming response was the inspiration for this book.
There are three parts in this book. The first part covers some of the more fundamental aspects of computer vision, the second describes a few interesting applications, and the third details specific approaches to facilitate programming for computer vision. This book is not intended to be a comprehensive coverage of computer vision; it can, however, be used as a complement to most computer vision textbooks.
We would like to thank all the contributors for all their hard work, and Bernard Goodwin for his support and enthusiasm for our book project.
Gérard Medioni, University of Southern California
Sing Bing Kang, Microsoft Research
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