Computer Vision and Machine Learning with RGB-D Sensors

Herausgegeben:Shao, Ling; Han, Jungong; Kohli, Pushmeet; Zhang, Zhengyou

• Gebundenes Buch

Produktbeschreibung

This book presents an interdisciplinary selection of cutting-edge research on RGB-D based computer vision. Features: discusses the calibration of color and depth cameras, the reduction of noise on depth maps and methods for capturing human performance in 3D; reviews a selection of applications which use RGB-D information to reconstruct human figures, evaluate energy consumption and obtain accurate action classification; presents an approach for 3D object retrieval and for the reconstruction of gas flow from multiple Kinect cameras; describes an RGB-D computer vision system designed to assist the visually impaired and another for smart-environment sensing to assist elderly and disabled people; examines the effective features that characterize static hand poses and introduces a unified framework to enforce both temporal and spatial constraints for hand parsing; proposes a new classifier architecture for real-time hand pose recognition and a novel hand segmentation and gesture recognition system.

Produktdetails

• Advances in Computer Vision and Pattern Recognition
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 86339766, 978-3-319-08650-7
• 2014
• Seitenzahl: 328
• Erscheinungstermin: 5. August 2014
• Englisch
• Abmessung: 241mm x 160mm x 24mm
• Gewicht: 661g
• ISBN-13: 9783319086507
• ISBN-10: 3319086502
• Artikelnr.: 40959183

Autorenporträt

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.

Inhaltsangabe

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.

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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.