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Multimodal Surveillance: Sensors, Algorithms, and Systems
 
 
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Multimodal Surveillance: Sensors, Algorithms, and Systems [Hardcover]

Zhigang Zhu (Editor), Thomas S. Huang (Editor)

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Book Description

1596931841 978-1596931848 August 2007
Multimodal surveillance is the next-generation of surveillance technology, combining traditional video and audio surveillance with state-of-the art sensors. These systems can identify at great distances approaching vehicles or scan crowds of people to pinpoint a specific individual. Once solely used by the military, they are becoming more widely used by law enforcement, business, and even high-end homeowners. This cutting-edge resource brings together the field's leading experts who guide researchers, designers, engineers, and developers through this multifaceted technology. It discusses the latest high-end sensors for extremely accurate surveillance, as well as low-cost sensing solutions. Designers get insight into new powerful algorithms that integrate sensor data to provide meaningful information. Engineers and developers benefit from an indepth examination of architectures, performance, and systems evaluation.

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About the Author

Zhigang Zhu is an associate professor in the Computer Science Department, City College of New York. An associate editor of Machine Vision and Applications Journal, he has been involved in organizing such meetings as the IEEE Virtual Reality Conference, the International Workshop on Digital and Computational Video, and the IEEE International Workshop on Image and Video Registration. Thomas Huang is the William L. Everitt Distinguished Professor of the Department of Electrical and Computer Engineering and of the Coordinated Science Lab at the University of Illinois at Champaign-Urbana. He has garnered many professional honors including the Honda Lifetime Achievement Award, IEEE Third Millennium Medal, and the IEEE Jack S. Kilby Signal Processing Medal. He holds an M.S. and Sc.D. in electrical engineering from M.I.T.

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Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
multimodal surveillance system, ordinal features, sensor fusion, biometric authentication, palmprint database, fingerprint system, multimodal workbench, mobile human surveillance, metal cake box, chronicling system, signal return strengths, smart videoconferencing, model transformation strategy, airborne mesh, multimodal air, matcher weighting, automatic surveillance application, ordinal filters, multimodal tracker, gait recognition algorithm, ear biometrics, acoustic speech recognizer, remote hearing, speech dataset, audiovisual speech recognition system
Key Phrases - Capitalized Phrases (CAPs): (learn more)
International Conference, New York, Computer Vision, Modeling of Cities, Systems Issues, Audio-Assisted Cameras, Audiovisual Speech Recognition, Multimodal Sensor, Technical Report, Signal Processing, Distributed Multimodal Surveillance, Monte Carlo, Computer Society Conference, Pattern Recognition, Image Processing, Multimodal Biometrics Involving the Human Ear, User Interfaces, University of California, System Issues, Matcher User Weighting Fusion, International Workshop, Header Cmds, The Polytec, World Wide Web Consortium, Large-Scale Video Surveillance
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Front Cover | Table of Contents | First Pages | Index | Surprise Me!
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