Springer Consumer Depth Cameras for Computer Vision: Research Topics and Applications (Advances in Computer Vision and Pattern Recognition)

Product Description 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. Review 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) From the Back Cover 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. 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.