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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. A500d 学位論文
  3. 博士論文(情科博・論情科博)

A generative learning method for low-resolution character recognition

http://hdl.handle.net/2237/11663
http://hdl.handle.net/2237/11663
aae47b39-270f-458d-9fd6-accf3683729a
名前 / ファイル ライセンス アクション
thesis-ishida.pdf thesis-ishida.pdf (1.7 MB)
Item type 学位論文 / Thesis or Dissertation(1)
公開日 2009-05-01
タイトル
タイトル A generative learning method for low-resolution character recognition
言語 en
著者 Ishida, Hiroyuki

× Ishida, Hiroyuki

WEKO 29683

en Ishida, Hiroyuki

Search repository
石田, 皓之

× 石田, 皓之

WEKO 29684

ja 石田, 皓之

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
抄録
内容記述 In this thesis, a training method for the low-resolution character recognition task is proposed. It is named “Generative learning method,” since the training images are generated artificially from an original image. The generative learning method is applied to camera-based character recognition and traffic sign recognition. Recognition technologies using digital cameras have gained considerable interest in recent years. Provided that these technologies come into practical use, there could be useful applications for camera-equipped devices. However, even with the improvements of digital cameras, the quality of captured images is still insufficient for the recognition in many practical cases. This work focuses on the training of image degradation characteristics. Recognition methods presented in this thesis are based on the generative learning method in which training images are artificially generated. Conventional approaches used camera-captured images as training data, which required exhaustive collection of the sample images by actual capturing. The proposed generative learning method, instead, allows to obtain these training images based on a small set of actual images. Since the training images need to be generated on the basis of actual degradation characteristics, the estimation step of the degradation characteristics is introduced. This framework is applied to three applications . character recognition, text recognition, and traffic sign recognition. In the camera-based character recognition application, optical blur and the vibration of hand-held cameras seriously affect the recognition accuracy. The proposed method copes with the optical blur and the motion blur by generating degraded training images. They are generated using a PSF (Point Spread Function) that preserves the actual blur characteristics. In the text recognition application, segmentation of characters is an unavoidable problem. It is difficult especially in the case where the image resolution is low. The proposed method copes with this problem by introducing a segmentation model to the generative learning method. In addition to characters, patterns of spaces between two adjacent characters are generated and used for accurate segmentation of the characters. In the traffic sign recognition application, various factors influence the captured traffic sign images. In the proposed framework, degradation parameters are defined for the simulation of these degradation factors. The distribution of the degradation parameters are estimated from actual images because it can produce appropriate parameters for generating the training images. Results obtained here have proven that the proposed generative learning method is effective for the applications suffering from various image degradations.
言語 en
内容記述タイプ Abstract
内容記述
内容記述 名古屋大学博士学位論文 学位の種類:博士(情報科学) (課程) 学位授与年月日:平成21年3月25日
言語 ja
内容記述タイプ Other
言語
言語 eng
資源タイプ
資源 http://purl.org/coar/resource_type/c_db06
タイプ doctoral thesis
書誌情報
発行日 2009-03-25
学位名
言語 ja
学位名 博士(情報科学)
学位授与機関
学位授与機関識別子Scheme kakenhi
学位授与機関識別子 13901
言語 ja
学位授与機関名 名古屋大学
言語 en
学位授与機関名 Nagoya University
学位授与年度
学位授与年度 2008
学位授与年月日
学位授与年月日 2009-03-25
学位授与番号
学位授与番号 甲第8396号
フォーマット
application/pdf
著者版フラグ
値 publisher
URI
識別子 http://hdl.handle.net/2237/11663
識別子タイプ HDL
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