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https://hdl.handle.net/20.500.14094/90004841
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2024-05-06
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90004841 (fulltext)
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メタデータID
90004841
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open access
出版タイプ
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タイトル
Coarse-to-fine online learning for hand segmentation in egocentric video
著者
Zhao, Ying ; Luo, Zhiwei ; Quan, Changqin
著者名
Zhao, Ying
著者ID
A0954
研究者ID
1000070242914
KUID
https://kuid-rm-web.ofc.kobe-u.ac.jp/search/detail?systemId=bfeb2452f2e7fe9d520e17560c007669
著者名
Luo, Zhiwei
羅, 志偉
ラ, シイ
所属機関名
システム情報学研究科
著者ID
A1010
研究者ID
1000000749898
KUID
https://kuid-rm-web.ofc.kobe-u.ac.jp/search/detail?systemId=af361d61575aed42520e17560c007669
著者名
Quan, Changqin
全, 昌勤
ゼン, ショウキン
所属機関名
システム情報学研究科
収録物名
Eurasip Journal on Image and Video Processing
巻(号)
2018
ページ
20-20
出版者
SpringerOpen
刊行日
2018-04-03
公開日
2018-05-08
抄録
Hand segmentation is one of the most fundamental and crucial steps for egocentric human-computer interaction. The special egocentric view brings new challenges to hand segmentation tasks, such as the unpredictable environmental conditions. The performance of traditional hand segmentation methods depend on abundant manually labeled training data. However, these approaches do not appropriately capture the whole properties of egocentric human-computer interaction for neglecting the user-specific context. It is only necessary to build a personalized hand model of the active user. Based on this observation, we propose an online-learning hand segmentation approach without using manually labeled data for training. Our approach consists of top-down classifications and bottom-up optimizations. More specifically, we divide the segmentation task into three parts, a frame-level hand detection which detects the presence of the interactive hand using motion saliency and initializes hand masks for online learning, a superpixel-level hand classification which coarsely segments hand regions from which stable samples are selected for next level, and a pixel-level hand classification which produces a fine-grained hand segmentation. Based on the pixel-level classification result, we update the hand appearance model and optimize the upper layer classifier and detector. This online-learning strategy makes our approach robust to varying illumination conditions and hand appearances. Experimental results demonstrate the robustness of our approach.
キーワード
Hand detection
Hand segmentation
Egocentric
Unsupervised online learning
カテゴリ
システム情報学研究科
学術雑誌論文
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© The Author(s). 2018
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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資源タイプ
journal article
言語
English (英語)
ISSN
1687-5176
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eISSN
1687-5281
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関連情報
DOI
https://doi.org/10.1186/s13640-018-0262-1
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