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Titlebook: Computer Vision Applications; Third Workshop, WCVA Chetan Arora,Kaushik Mitra Conference proceedings 2019 Springer Nature Singapore Pte Ltd

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发表于 2025-3-21 16:41:55 | 显示全部楼层 |阅读模式
书目名称Computer Vision Applications
副标题Third Workshop, WCVA
编辑Chetan Arora,Kaushik Mitra
视频videohttp://file.papertrans.cn/235/234018/234018.mp4
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Computer Vision Applications; Third Workshop, WCVA Chetan Arora,Kaushik Mitra Conference proceedings 2019 Springer Nature Singapore Pte Ltd
描述This book constitutes the refereed proceedings of the third Workshop on Computer Vision Applications, WCVA 2018, held in Conjunction with ICVGIP 2018, in Hyderabad, India, in December 2018. The 10 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers focus on computer vision; industrial applications; medical applications; and social applications.
出版日期Conference proceedings 2019
关键词artificial intelligence; computer hardware; computer network; computer systems; computer vision; educatio
版次1
doihttps://doi.org/10.1007/978-981-15-1387-9
isbn_softcover978-981-15-1386-2
isbn_ebook978-981-15-1387-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Singapore Pte Ltd. 2019
The information of publication is updating

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Computer Vision Applications978-981-15-1387-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
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Communications in Computer and Information Sciencehttp://image.papertrans.cn/c/image/234018.jpg
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Conference proceedings 2019 in Hyderabad, India, in December 2018. The 10 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers focus on computer vision; industrial applications; medical applications; and social applications.
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https://doi.org/10.1057/9780230119130utomotive scenes. Typically depth is already computed in an automotive system to localize objects and path planning and thus can be leveraged for semantic segmentation. We construct two networks that serve as a baseline for comparison which are “RGB only” and “Depth only”, and we investigate the imp
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https://doi.org/10.1007/978-94-011-2805-6nmental studies. Recognizing bird species are difficult due to the challenges of discriminative region localization and fine-grained feature learning. In this paper, we have introduced a Transfer learning based method with multistage training. We have used both Pre-Trained Mask-RCNN and a ensemble m
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