Consumer Depth Cameras for Computer Vision: Research Topics and Applications

Consumer Depth Cameras for Computer Vision: Research Topics and Applications


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The potential of consumer depth cameras extends well beyond entertainment and gaming, to real-world commercial applications. This authoritative text 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. 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.

Product Details

ISBN-13: 9781447146391
Publisher: Springer London
Publication date: 10/04/2012
Series: Advances in Computer Vision and Pattern Recognition
Edition description: 2013
Pages: 210
Product dimensions: 6.10(w) x 9.25(h) x 0.03(d)

About the Author

Dr. Andrea Fossati and Dr. Helmut Grabner are post-doctoral researchers in the Computer Vision Laboratory at ETH Zurich, Switzerland.

Dr. Juergen Gall is a Senior Researcher at the Max Planck Institute for Intelligent Systems, Tübingen, Germany.

Dr. Xiaofeng Ren is a Research Scientist at the Intel Science and Technology Center for Pervasive Computing, Intel Labs, and an Affiliate Assistant Professor at the Department of Computer Science and Engineering of the University of Washington, Seattle, WA, USA.

Dr. Kurt Konolige is a Senior Researcher at Industrial Perception Inc., Palo Alto, CA, USA.

Table of Contents

Part I: 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 Israël, and Aurélien Plyer

Part II: 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 Müller, 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 Kıraç, Yunus Emre Kara, and Lale Akarun

Part III: RGB-D Datasets

A Category-Level 3D Object Dataset: Putting the Kinect to Work
Allison Janoch, Sergey Karayev, Yangqing Jia, Jonathan T. Barron, Mario Fritz, Kate Saenko, and Trevor Darrell

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

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