Computer Vision and Machine Learning with RGB-D Sensors

Part I: Surveys.- 3D Depth Cameras in Vision: Benefits and Limitations of the Hardware.- A State-of-the-Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D Datasets.- Part II: Reconstruction, Mapping and Synthesis.- Calibration Between Depth and Color Sensors for Commodity Depth Cameras.- Depth Map Denoising via CDT-Based Joint Bilateral Filter.- Human Performance Capture Using Multiple Handheld Kinects.- Human Centered 3D Home Applications via Low-Cost RGBD Cameras.- Matching of 3D Objects Based on 3D Curves.- Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinects.- Part III: Detection, Segmentation and Tracking.- RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons.- RGB-D Human Identification and Tracking in a Smart Environment.- Part IV: Learning-Based Recognition.- Feature Descriptors for Depth-Based Hand Gesture Recognition.- Hand Parsing and Gesture Recognition with a Commodity Depth Camera.- Learning Fast Hand Pose Recognition.- Real time Hand-Gesture Recognition Using RGB-D Sensor.

Dr. Ling Shao is a Senior Lecturer (Associate Professor) in the Department of Electronic and Electrical Engineering at the University of Sheffield, UK. His publications include the Springer title Multimedia Interaction and Intelligent User Interfaces.

Dr. Jungong Han is a Senior Scientist at Civolution Technology, Eindhoven, and a Guest Researcher at the Eindhoven University of Technology, Netherlands.

Dr. Pushmeet Kohli is a Senior Researcher in the Machine Learning and Perception Group at Microsoft Research Cambridge and an Associate in the Psychometrics Centre at the University of Cambridge, UK.

Dr. Zhengyou Zhang, IEEE Fellow and ACM Fellow, is a Principal Researcher and Research Manager of the Multimedia, Interaction, and Communication Group at Microsoft Research Redmond, WA, USA.