Computer Vision and Machine Learning with RGB-D Sensors

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