Consumer Depth Cameras for Computer Vision

Research Topics and Applications
Softcover Nachdruck of the original 1. Auflage 2013
ISBN: 978-1-4471-6977-2
Verlag: Springer

Buch, Englisch, 210 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 3933 g

Reihe: Advances in Computer Vision and Pattern Recognition

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.

Zielgruppe

Research

Autoren/Hrsg.

Fossati, Andrea

Gall, Juergen

Konolige, Kurt

Grabner, Helmut

Ren, Xiaofeng

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

Part I: 3D Registration and Reconstruction.- 3D with Kinect.- Real-Time RGB-D Mapping and 3-D Modeling on the GPU using the Random Ball Cover.- A Brute Force Approach to Depth Camera Odometry.- Part II: Human Body Analysis.- Key Developments in Human Pose Estimation for Kinect.- A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera.- Home 3D Body Scans from a Single Kinect.- Real-Time Hand Pose Estimation using Depth Sensors.- Part III: RGB-D Datasets.- A Category-Level 3D Object Dataset: Putting the Kinect to Work.- RGB-D Object Recognition: Features, Algorithms, and a Large Scale Benchmark.- RGBD-HuDaAct: A Color-Depth Video Database for Human Daily Activity Recognition.

Über Autor(innen)

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.