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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

Construction of Cascaded Traffic Sign Detector Using Generative Learning

http://hdl.handle.net/2237/13961
http://hdl.handle.net/2237/13961
36248ebc-35e1-46bd-87c6-97a512a8f557
名前 / ファイル ライセンス アクション
doman.pdf doman.pdf (499.0 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2010-08-04
タイトル
タイトル Construction of Cascaded Traffic Sign Detector Using Generative Learning
言語 en
著者 Doman, Keisuke

× Doman, Keisuke

WEKO 38339

en Doman, Keisuke

Search repository
Deguchi, Daisuke

× Deguchi, Daisuke

WEKO 38340

en Deguchi, Daisuke

Search repository
Takahashi, Tomokazu

× Takahashi, Tomokazu

WEKO 38341

en Takahashi, Tomokazu

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Mekada, Yoshito

× Mekada, Yoshito

WEKO 38342

en Mekada, Yoshito

Search repository
Ide, Ichiro

× Ide, Ichiro

WEKO 38343

en Ide, Ichiro

Search repository
Murase, Hiroshi

× Murase, Hiroshi

WEKO 38344

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 ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
抄録
内容記述 We propose a method for construction of a cascaded traffic sign detector. Viola et al. have proposed a robust and extremely rapid object detection method based on a boosted cascade of simple feature classifiers. To obtain a high detection accuracy in real environment, it is necessary to train the classifier with a set of learning images which contain various appearances of detection targets. However, collecting the traffic sign images manually for training takes much cost. Therefore, we use a generative learning method for constructing the traffic sign detector. In this paper, shape, texture and color changes are considered in the generative learning. By this method, the performance of the traffic sign detection improves and the cost of collecting the training images is reduced at the same time. Experimental results using car-mounted camera images showed the effectiveness of the proposed method.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 IEEE
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/10.1109/ICICIC.2009.148
ISBN
関連タイプ isPartOf
識別子タイプ ISBN
関連識別子 978-1-4244-5543-0
書誌情報 en : Fourth International Conference on Innovative Computing, Information and Control (ICICIC)

p. 889-892, 発行日 2009-12-07
フォーマット
application/pdf
著者版フラグ
値 publisher
URI
識別子 http://hdl.handle.net/2237/13961
識別子タイプ HDL
URI
識別子 http://dx.doi.org/10.1109/ICICIC.2009.148
識別子タイプ DOI
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