Consumer Depth Cameras for Computer Vision: Research Topics and Applications

ISBN-13: 9781447169772 / Angielski / Miękka / 2016 / 210 str.

Andrea Fossati; Juergen Gall; Helmut Grabner

Opis

This up-to-date and authoritative book surveys the most promising Kinect-based research activities, discussing current challenges to the adaptation of consumer depth cameras, and showcasing exciting applications that extend far beyond entertainment and gaming.

Recenzje

From the reviews:

"Consumer Depth Cameras for Computer Vision is among the first few written from a research point of view. It is based on research published in the Workshop on Consumer Depth Cameras and includes some of the notable researchers in the field and the original team behind Kinect itself. It's an excellent resource for researchers working in computer vision and robotic vision or for students having knowledge of vision who wish to start working with Kinect. ... highly recommend this book to any researcher of Kinect." (Owais Mehmood, IAPR Newsletter, Vol. 35 (3-2), July, 2013)

Spis treści

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

O autorze

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.

Blurb

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.