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Titlebook: Computer Vision – ECCV 2018 Workshops; Munich, Germany, Sep Laura Leal-Taixé,Stefan Roth Conference proceedings 2019 Springer Nature Switze

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書(shū)目名稱Computer Vision – ECCV 2018 Workshops
副標(biāo)題Munich, Germany, Sep
編輯Laura Leal-Taixé,Stefan Roth
視頻videohttp://file.papertrans.cn/235/234203/234203.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Computer Vision – ECCV 2018 Workshops; Munich, Germany, Sep Laura Leal-Taixé,Stefan Roth Conference proceedings 2019 Springer Nature Switze
描述The six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in?conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good?orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and?novel applications..
出版日期Conference proceedings 2019
關(guān)鍵詞computer vision; machine learning; vision for robotics; natural language processing; activity recognitio
版次1
doihttps://doi.org/10.1007/978-3-030-11024-6
isbn_softcover978-3-030-11023-9
isbn_ebook978-3-030-11024-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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Computer Vision for Medical Infant Motion Analysis: State of the Art and RGB-D Data SetSMIL). We map real infant movements to the SMIL model with realistic shapes and textures, and generate RGB and depth images with precise ground truth 2D and 3D joint positions. We evaluate our data set with state-of-the-art methods for 2D pose estimation in RGB images and for 3D pose estimation in d
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DrawInAir: A Lightweight Gestural Interface Based on Fingertip Regressionclassification. We highlight how a model, that is separately trained to regress fingertip in conjunction with a classifier trained on limited classification data, would perform better over . models. We also propose a dataset of 10 egocentric pointing gestures designed for AR applications for testing
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