Consumer Depth Cameras for Computer Vision
Consumer Depth Cameras for Computer Vision Research Topics and Applications / [electronic resource] :
edited by Andrea Fossati, Juergen Gall, Helmut Grabner, Xiaofeng Ren, Kurt Konolige.
- XVI, 210 p. 109 illus., 106 illus. in color. online resource.
- Advances in Computer Vision and Pattern Recognition, 2191-6586 .
- Advances in Computer Vision and Pattern Recognition, .
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.
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.
9781447146407
10.1007/978-1-4471-4640-7 doi
Computer science.
Computer graphics.
Image processing.
Pattern recognition.
Computer Science.
Image Processing and Computer Vision.
Pattern Recognition.
Computer Graphics.
TA1637-1638 TA1634
006.6 006.37
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.
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.
9781447146407
10.1007/978-1-4471-4640-7 doi
Computer science.
Computer graphics.
Image processing.
Pattern recognition.
Computer Science.
Image Processing and Computer Vision.
Pattern Recognition.
Computer Graphics.
TA1637-1638 TA1634
006.6 006.37