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アイテム
Human Wearable Attribute Recognition Using Probability-Map-Based Decomposition of Thermal Infrared Images
http://hdl.handle.net/2237/26773
http://hdl.handle.net/2237/267733abce8c3-654f-48ef-acc6-39bfbe209758
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
e100-a_3_854.pdf (3.5 MB)
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| 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× KAWANISHI, Yasutomo× DEGUCHI, Daisuke× TAKAHASHI, Tomokazu× MEKADA, Yoshito× IDE, Ichiro× MURASE, Hiroshi |
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| 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 |
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| 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 | |||||
