Articles liés à Computer Vision and Machine Learning with RGB-D Sensors
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Synopsis
Part I: Surveys
3D Depth Cameras in Vision: Benefits and Limitations of the Hardware
Achuta Kadambi, Ayush Bhandari and Ramesh Raskar
A State-of-the-Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D Datasets
Kai Berger
Part II: Reconstruction, Mapping and Synthesis
Calibration Between Depth and Color Sensors for Commodity Depth Cameras
Cha Zhang and Zhengyou Zhang
Depth Map Denoising via CDT-Based Joint Bilateral Filter
Andreas Koschan and Mongi Abidi
Human Performance Capture Using Multiple Handheld Kinects
Yebin Liu, Genzhi Ye, Yangang Wang, Qionghai Dai and Christian Theobalt
Human Centered 3D Home Applications via Low-Cost RGBD Cameras
Zhenbao Liu, Shuhui Bu and Junwei Han
Matching of 3D Objects Based on 3D Curves
Christian Feinen, Joanna Czajkowska, Marcin Grzegorzek and Longin Jan Latecki
Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinects
Kai Berger, Marc Kastner, Yannic Schroeder and Stefan Guthe
Part III: Detection, Segmentation and Tracking
RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons
Yingli Tian
RGB-D Human Identification and Tracking in a Smart Environment
Jungong Han and Junwei Han
Part IV: Learning-Based Recognition
Feature Descriptors for Depth-Based Hand Gesture Recognition
Fabio Dominio, Giulio Marin, Mauro Piazza and Pietro Zanuttigh
Hand Parsing and Gesture Recognition with a Commodity Depth Camera
Hui Liang and Junsong Yuan
Learning Fast Hand Pose Recognition
Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, Daniel Freedman, Simon Stachniak and Cem Keskin
Realtime Hand-Gesture Recognition Using RGB-D Sensor
Yuan Yao, Fan Zhang and Yun Fu
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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