| 书目名称 | Time-of-Flight and Depth Imaging. Sensors, Algorithms and Applications | | 副标题 | Dagstuhl Seminar 201 | | 编辑 | Marcin Grzegorzek,Christian Theobalt,Andreas Kolb | | 视频video | | | 概述 | State-of-the-art research on time-of-flight (ToF) depth imaging contributions.Prominent selection of leading researchers in the field of ToF includes several survey chapters.Deals with the following a | | 丛书名称 | Lecture Notes in Computer Science | | 图书封面 |  | | 描述 | .Cameras for 3D depth imaging, using either time-of-flight (ToF) or structured light sensors, have received a lot of attention recently and have been improved considerably over the last few years. The present techniques make full-range 3D data available at video frame rates, and thus pave the way for a much broader application of 3D vision systems..A series of workshops have closely followed the developments within ToF imaging over the years. Today, depth imaging workshops can be found at every major computer vision conference. The papers presented in this volume stem from a seminar on Time-of-Flight Imaging held at Schloss Dagstuhl in October 2012. They cover all aspects of ToF depth imaging, from sensors and basic foundations, to algorithms for low level processing, to important applications that exploit depth imaging. In addition, this book contains the proceedings of a workshop on Imaging New Modalities, which was held at the German Conference on Pattern Recognition in Saarbrücken, Germany, in September 2013. A state-of-the-art report on the Kinect sensor and its applications is followed by two reports on local and global ToF motion compensation and a novel depth capture system | | 出版日期 | Book 2013 | | 关键词 | ToF cameras; depth data; noise models; scene recognition; sensor calibration | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-642-44964-2 | | isbn_softcover | 978-3-642-44963-5 | | isbn_ebook | 978-3-642-44964-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 | | issn_series | 0302-9743 | | copyright | Springer-Verlag Berlin Heidelberg 2013 |
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发表于 2025-3-21 18:55:26
