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Computer Vision and Machine Learning with RGB-D Sensors

  • Livre Relié
  • 316 Nombre de pages
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The combination of high-resolution visual and depth sensing, supported by machine learning, opens up new opportunities to solve re... Lire la suite
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Description

The combination of high-resolution visual and depth sensing, supported by machine learning, opens up new opportunities to solve real-world problems in computer vision.

This authoritative text/reference presents an interdisciplinary selection of important, cutting-edge research on RGB-D based computer vision. Divided into four sections, the book opens with a detailed survey of the field, followed by a focused examination of RGB-D based 3D reconstruction, mapping and synthesis. The work continues with a section devoted to novel techniques that employ depth data for object detection, segmentation and tracking, and concludes with examples of accurate human action interpretation aided by depth sensors.

Topics and 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 innovative 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.

Researchers and practitioners working in computer vision, HCI and machine learning will find this to be a must-read text. The book also serves as a useful reference for graduate students studying computer vision, pattern recognition or multimedia.

Describes recent advances in RGB-D based computer vision algorithms, with an emphasis on advanced machine learning techniques for interpreting the RGBD information

Covers a range of different techniques from computer vision, machine learning, audio, speech and signal processing, communications, artificial intelligence and media technology

Includes contributions from leading researchers in this area, with strong industrial-research experience of the practical issues



Auteur

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.



Contenu
Part I: Surveys3D Depth Cameras in Vision: Benefits and Limitations of the HardwareAchuta Kadambi, Ayush Bhandari and Ramesh RaskarA State-of-the-Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D DatasetsKai BergerPart II: Reconstruction, Mapping and SynthesisCalibration Between Depth and Color Sensors for Commodity Depth CamerasCha Zhang and Zhengyou ZhangDepth Map Denoising via CDT-Based Joint Bilateral FilterAndreas Koschan and Mongi AbidiHuman Performance Capture Using Multiple Handheld KinectsYebin Liu, Genzhi Ye, Yangang Wang, Qionghai Dai and Christian TheobaltHuman Centered 3D Home Applications via Low-Cost RGBD CamerasZhenbao Liu, Shuhui Bu and Junwei HanMatching of 3D Objects Based on 3D CurvesChristian Feinen, Joanna Czajkowska, Marcin Grzegorzek and Longin Jan LateckiUsing Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple KinectsKai Berger, Marc Kastner, Yannic Schroeder and Stefan GuthePart III: Detection, Segmentation and TrackingRGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired PersonsYingli TianRGB-D Human Identification and Tracking in a Smart EnvironmentJungong Han and Junwei HanPart IV: Learning-Based RecognitionFeature Descriptors for Depth-Based Hand Gesture RecognitionFabio Dominio, Giulio Marin, Mauro Piazza and Pietro ZanuttighHand Parsing and Gesture Recognition with a Commodity Depth CameraHui Liang and Junsong YuanLearning Fast Hand Pose RecognitionEyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, Daniel Freedman, Simon Stachniak and Cem KeskinRealtime Hand-Gesture Recognition Using RGB-D SensorYuan Yao, Fan Zhang and Yun Fu

Informations sur le produit

Titre: Computer Vision and Machine Learning with RGB-D Sensors
Éditeur:
Code EAN: 9783319086507
ISBN: 978-3-319-08650-7
Format: Livre Relié
Editeur: Springer, Berlin
Genre: Informatique
nombre de pages: 316
Poids: 568g
Taille: H18mm x B242mm x T167mm
Année: 2014
Auflage: 2014

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