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Title Consumer depth cameras for computer vision : research topics and applications / Andrea Fossati, Juergen Gall, Helmut Grabner, Xiaofeng Ren, Kurt Konolige, editors.
Imprint London ; New York : Springer, 2013.

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Series Advances in computer vision and pattern recognition
Advances in computer vision and pattern recognition.
Subject Computer vision.
Depth perception.
Pattern recognition systems.
Artificial Intelligence.
Depth Perception.
Pattern Recognition, Automated.
Alt Name Fossati, Andrea,
Gall, Juergen,
Grabner, Helmut,
Ren, Xiaofeng, 1974-
Konolige, Kurt,
Description 1 online resource (xvi, 210 pages) : illustrations (some color).
polychrome rdacc
Contents Finite element formulations -- Coupled finite element flow formulation -- Contact modeling -- Meshing and remeshing -- Parallelization of equation solvers -- Material, friction and contact characterization -- Applications.
Part 1. 3D Registration and Reconstruction -- 3D with Kinect / Jan Smisek, Michal Jancosek and Tomas Pajdla -- Real-Time RGB-D Mapping and 3-D Modeling on the GPU Using the Random Ball Cover / Sebastian Bauer, Jakob Wasza, Felix Lugauer, Dominik Neumann and Joachim Hornegger -- A Brute Force Approach to Depth Camera Odometry / Jonathan Israel and Aurelien Plyer -- Part 2. Human Body Analysis -- Key Developments in Human Pose Estimation for Kinect / Pushmeet Kohli and Jamie Shotton -- A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera / Andreas Baak, Meinard Muller, Gaurav Bharaj, Hans-Peter Seidel and Christian Theobalt -- Home 3D Body Scans from a Single Kinect / Alexander Weiss, David Hirshberg and Michael J. Black -- Real Time Hand Pose Estimation Using Depth Sensors / Cem Keskin, Furkan Krac, Yunus Emre Kara and Lale Akarun -- Part 3. RGB-D Datasets -- A Category-Level 3D Object Dataset: Putting the Kinect to Work / Allison Janoch, Sergey Karayev, Yangqing Jia, Jonathan T. Barron and Mario Fritz, et al. -- RGB-D Object Recognition: Features, Algorithms, and a Large Scale Benchmark / Kevin Lai, Liefeng Bo, Xiaofeng Ren and Dieter Fox -- RGBD-HuDaAct: A Color-Depth Video Database for Human Daily Activity Recognition / Bingbing Ni, Gang Wang and Pierre Moulin.
Bibliography Note Includes bibliographical references and index.
Summary The launch of Microsoft's Kinect, the first high-resolution depth-sensing camera for the consumer market, generated considerable excitement not only among computer gamers, but also within the global community of computer vision researchers. The potential of consumer depth cameras extends well beyond entertainment and gaming, to real-world commercial applications such virtual fitting rooms, training for athletes, and assistance for the elderly. This authoritative text/reference reviews the scope and impact of this rapidly growing field, describing the most promising Kinect-based research activities, discussing significant current challenges, and showcasing exciting applications. Topics and features: Presents contributions from an international selection of preeminent authorities in their fields, from both academic and corporate research Addresses the classic problem of multi-view geometry of how to correlate images from different viewpoints to simultaneously estimate camera poses and world points Examines human pose estimation using video-rate depth images for gaming, motion capture, 3D human body scans, and hand pose recognition for sign language parsing Provides a review of approaches to various recognition problems, including category and instance learning of objects, and human activity recognition With a Foreword by Dr. Jamie Shotton of Microsoft Research, Cambridge, UK This broad-ranging overview is a must-read for researchers and graduate students of computer vision and robotics wishing to learn more about the state of the art of this increasingly "hot" topic.
Note Print version record.
ISBN 9781447146407 (electronic bk.)
1447146409 (electronic bk.)
ISBN/ISSN 10.1007/978-1-4471-4640-7
OCLC # 813213128
Additional Format Print version: Consumer depth cameras for computer vision. London ; New York : Springer, 2013. 1st ed. 9781447146391 (DLC) 2012950032 (OCoLC)823211302

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