Consumer Depth Cameras for Computer Vision

Research Topics and Applications

By Andrea Fossati , Juergen Gall , Helmut Grabner , Xiaofeng Ren , Kurt Konolige

Consumer Depth Cameras for Computer Vision Cover Image

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.

Full Description

  • ISBN13: 978-1-4471-4639-1
  • 228 Pages
  • User Level: Science
  • Publication Date: October 3, 2012
  • Available eBook Formats: PDF
  • eBook Price: $109.00
Buy eBook Buy Print Book Add to Wishlist

Related Titles

Full Description
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.
Table of Contents

Table of Contents

  1. Part I: 3D Registration and Reconstruction.
  2. 3D with Kinect.
  3. Real
  4. Time RGB
  5. D Mapping and 3
  6. D Modeling on the GPU using the Random Ball Cover.
  7. A Brute Force Approach to Depth Camera Odometry.
  8. Part II: Human Body Analysis.
  9. Key Developments in Human Pose Estimation for Kinect.
  10. A Data
  11. Driven Approach for Real
  12. Time Full Body Pose Reconstruction from a Depth Camera.
  13. Home 3D Body Scans from a Single Kinect.
  14. Real
  15. Time Hand Pose Estimation using Depth Sensors.
  16. Part III: RGB
  17. D Datasets.
  18. A Category
  19. Level 3D Object Dataset: Putting the Kinect to Work.
  20. RGB
  21. D Object Recognition: Features, Algorithms, and a Large Scale Benchmark.
  22. RGBD
  23. HuDaAct: A Color
  24. Depth Video Database for Human Daily Activity Recognition.
Errata

Please Login to submit errata.

No errata are currently published