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  1. A500 情報学部/情報学研究科・情報文化学部・情報科学研究科
  2. A500a 雑誌掲載論文
  3. 学術雑誌

Human Wearable Attribute Recognition Using Probability-Map-Based Decomposition of Thermal Infrared Images

http://hdl.handle.net/2237/26773
http://hdl.handle.net/2237/26773
3abce8c3-654f-48ef-acc6-39bfbe209758
名前 / ファイル ライセンス アクション
e100-a_3_854.pdf e100-a_3_854.pdf (3.5 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2017-07-05
タイトル
タイトル Human Wearable Attribute Recognition Using Probability-Map-Based Decomposition of Thermal Infrared Images
言語 en
著者 KRESNARAMAN, Brahmastro

× KRESNARAMAN, Brahmastro

WEKO 72686

en KRESNARAMAN, Brahmastro

Search repository
KAWANISHI, Yasutomo

× KAWANISHI, Yasutomo

WEKO 72687

en KAWANISHI, Yasutomo

Search repository
DEGUCHI, Daisuke

× DEGUCHI, Daisuke

WEKO 72688

en DEGUCHI, Daisuke

Search repository
TAKAHASHI, Tomokazu

× TAKAHASHI, Tomokazu

WEKO 72689

en TAKAHASHI, Tomokazu

Search repository
MEKADA, Yoshito

× MEKADA, Yoshito

WEKO 72690

en MEKADA, Yoshito

Search repository
IDE, Ichiro

× IDE, Ichiro

WEKO 72691

en IDE, Ichiro

Search repository
MURASE, Hiroshi

× MURASE, Hiroshi

WEKO 72692

en MURASE, Hiroshi

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 copyright(c)2017 IEICE
キーワード
主題Scheme Other
主題 thermal infrared
キーワード
主題Scheme Other
主題 wearable attribute
キーワード
主題Scheme Other
主題 recognition
キーワード
主題Scheme Other
主題 decomposition
キーワード
主題Scheme Other
主題 probability map
抄録
内容記述 This paper addresses the attribute recognition problem, a field of research that is dominated by studies in the visible spectrum. Only a few works are available in the thermal spectrum, which is fundamentally different from the visible one. This research performs recognition specifically on wearable attributes, such as glasses and masks. Usually these attributes are relatively small in size when compared with the human body, on top of a large intra-class variation of the human body itself, therefore recognizing them is not an easy task. Our method utilizes a decomposition framework based on Robust Principal Component Analysis (RPCA) to extract the attribute information for recognition. However, because it is difficult to separate the body and the attributes without any prior knowledge, noise is also extracted along with attributes, hampering the recognition capability. We made use of prior knowledge; namely the location where the attribute is likely to be present. The knowledge is referred to as the Probability Map, incorporated as a weight in the decomposition by RPCA. Using the Probability Map, we achieve an attribute-wise decomposition. The results show a significant improvement with this approach compared to the baseline, and the proposed method achieved the highest performance in average with a 0.83 F-score.
言語 en
内容記述タイプ Abstract
出版者
言語 ja
出版者 一般社団法人電子情報通信学会
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1587/transfun.E100.A.854
ISSN
収録物識別子タイプ PISSN
収録物識別子 0916-8508
書誌情報 en : IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

巻 E100, 号 3, p. 854-864, 発行日 2017-03-01
著者版フラグ
値 publisher
URI
識別子 http://doi.org/10.1587/transfun.E100.A.854
識別子タイプ DOI
URI
識別子 https://www.ieice.org/jpn/books/transaction.html
識別子タイプ URI
URI
識別子 http://hdl.handle.net/2237/26773
識別子タイプ HDL
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