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Titlebook: Computer Vision and Machine Learning with RGB-D Sensors; Ling Shao,Jungong Han,Zhengyou Zhang Book 2014 Springer International Publishing

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發(fā)表于 2025-3-21 20:09:09 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computer Vision and Machine Learning with RGB-D Sensors
編輯Ling Shao,Jungong Han,Zhengyou Zhang
視頻videohttp://file.papertrans.cn/235/234070/234070.mp4
概述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 techniqu
叢書名稱Advances in Computer Vision and Pattern Recognition
圖書封面Titlebook: Computer Vision and Machine Learning with RGB-D Sensors;  Ling Shao,Jungong Han,Zhengyou Zhang Book 2014 Springer International Publishing
描述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.
出版日期Book 2014
關鍵詞Computer Vision; Consumer Electronics; Human-Computer Interaction; Intelligent Systems; Machine Learning
版次1
doihttps://doi.org/10.1007/978-3-319-08651-4
isbn_softcover978-3-319-38105-3
isbn_ebook978-3-319-08651-4Series ISSN 2191-6586 Series E-ISSN 2191-6594
issn_series 2191-6586
copyrightSpringer International Publishing Switzerland 2014
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Calibration Between Depth and Color Sensors for Commodity Depth Camerasation information between the color and the depth cameras. Traditional checkerboard-based calibration schemes fail to work well for the depth camera, since its corner features cannot be reliably detected in the depth image. In this chapter, we present a maximum likelihood solution for the joint dept
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Human Performance Capture Using Multiple Handheld Kinects. The reconstructed 3D performance can be used for character animation and free-viewpoint video. While most of the available performance capture approaches rely on a 3D video studio with tens of RGB cameras, this chapter presents a method for marker-less performance capture of single or multiple hum
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Matching of 3D Objects Based on 3D Curvesh device (RGB-D). Our processing pipeline consists of several steps. In the preprocessing step, we first detect edges in the depth image and merge them to 2D object curves which allows a back-projection to 3D space. Then, we estimate a local coordinate system for these 3D curves. In the next step, d
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