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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 電信工程學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41532
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dc.contributor.advisor陳銘憲(Ming-Syan Chen)
dc.contributor.authorChen-Chien Linen
dc.contributor.author林陳鍵zh_TW
dc.date.accessioned2021-06-15T00:21:57Z-
dc.date.available2009-02-12
dc.date.copyright2009-02-12
dc.date.issued2009
dc.date.submitted2009-02-03
dc.identifier.citationBibliography
[1] Au optronics corp., http://auo.com/auodev/technology.php?sec=tftintro.
[2] Cbc corporated, http://www.cbc-mirunet.com/.
[3] Denso wave incorporated, http://www.deso-wave.com/qrcode/index-e.html.
[4] Htc corporation, http://www.htc.com/tw/.
[5] Itmedia incorporated, http://www.itmedia.co.jp/bizid/articles/0606/22/news059.html.
[6] Microsoft developer network, http://msdn.microsoft.com/zh-tw/library/bb158486(en-us).aspx.
[7] Taiwan agriculture and food traceability system, http://taft.coa.gov.tw/welcome.swf.
[8] Yahoo japan, http://sp.mobile.yahoo.co.jp/mobile/.
[9] ISO/IEC 18004:2000. Information technology-Automatic identification and data capture
techniques-Bar code symbology-QR Code. 2000.
[10] B.M. J. Buades, A. Coll. A non-local alorithm for image denoising. Computer Vision and Pattern
Recognition,2005.IEEE Computer Society Conference, 2(2):60–65, 2005.
[11] K. H.Kato. 2d barcodes for mobile phones. Mobile Technology, page 8, 2005.
[12] P.-Y. H. S.-S. C. F.-C. Huang. Generic 2-d gaussian smoothing filter for noisy image processing.
TENCON 2007-2007 IEEE Region 10 Conference, (1-4):1–4, 2008.
[13] K. Kamijo, N. Kamijo, and M. Sakamoto. Electronic clipping system with invisible barcodes.
Proceedings of ACM International Conference on Multimedia, 2006.
[14] C. Nokia Res. Center. A snapshot of research and applications. Pervasive Computing, 7:16–19,
2008.
[15] E. Ohbuchi, H. Hanaizumi, and L. A. Hock. Barcode readers using the camera device in mobile
phones. IEEE International Conference on Cyberworlds (CW04), 2004.
[16] E. Ottaviani, A. Pavan, M. Bottaz, E. Brunclli, F. Casclli, and M. Guerrero. A common image
processing framework for 2d barcode reading. IEEE International Conference on Image Processing
and Its Applications, 1999.
[17] S. Paris. A gentle introduction to bilateral filtering and its applications: Fixing gaussian blur: the
bilateral filter. SIGGRAPH, 2007.
[18] S. Paris. A gentle introduction to bilateral filtering and its applications: Gaussian blur. SIGGRAPH,
2007.
[19] S. Paris. A gentle introduction to bilateral filtering and its applications: Introduction. SIGGRAPH,
2007.
[20] Q. C. Technologies. Msm7201a mobile station modem user guide. (Rev.A), December 19,2008.
[21] R. Tomasi, c. Manduchi. Bilateral filtering for gray and color images. Computer Vision,1998.
Sixth International Conference, (1164):839–846, 2002.
[22] W.K.Pratt. Digital image processing.john wiley and sons,2001(third edition).
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41532-
dc.description.abstract近幾年來,隨著行動通訊裝置的快速發展與功能不斷的推陳出新,我們可以在任何時間,地點使用任何方法取得我們所需要的資訊,在眾多的方法中,又以QR code的應用是最受消費者所歡迎的,其中現在新的應用則是使用照相手機對著顯示器上所顯示的QR code 做拍照來獲得相關訊息。但是,隨著照像手機的照相解析度越來越高,當我們使用此高解析度的照相手機擷取顯示器的 QR code 時,常常會被顯示器上單位畫素所產生的干擾所影響,而造成無法解碼判讀的情況發生。
因此,我們提出一個有效的QR code雜訊濾除方法,使用此濾除方法可以有效地濾除上述所提的畫素干擾雜訊,又較不會影響圖像的清晰度,因此,可以提高 QR code的解碼判讀率,另外,我們也會將此濾除方式應用在嵌入式系統中,並與其他常見的影像濾波處理作比較。由實驗結果可知道,在經過我們的方法濾波處理後,被雜訊所干擾的圖像在解碼辨識失敗率上都有很明顯地改善許多。
zh_TW
dc.description.abstractIn the recent years, with the rapid advances in the wireless mobile communication technologies and applications, mobile users can obtain information of their requirement from anytime, anywhere, via wireless mobile devices. In these mobile phone applications, the QR-code applications are popular function. In recent applications, users can take a QR-code pattern from the displayer for obtaining their required information. However, if users use high-resolution camera modules in the mobile phone to take QR-code patterns from displayer, the taken image suffers from interference at the pixels from the displayer. Such interference affects QR-code recognition.
In this thesis, we propose QR-code denoisy filter to efficiently and effectively filter out these noises and also to preserve the edges of the image on the noisy QR-code patterns. This QR-code denoisy filter processing also can be applied to embedded mobile phones. We also conduct several experiments to evaluate the quality and the performance of our QR-code denoisy filter.
As compared to other filters, the experimental results show that our approach can achieve the higher QR-code recognition and a comparable running speed.
en
dc.description.provenanceMade available in DSpace on 2021-06-15T00:21:57Z (GMT). No. of bitstreams: 1
ntu-98-P95942006-1.pdf: 2240349 bytes, checksum: e3630f3a32bf72c613b4c9ac497f4598 (MD5)
Previous issue date: 2009
en
dc.description.tableofcontentsContents
1 Introduction...........................................1
2 Preliminaries..........................................3
2.1 Barcode Introduction................................3
2.2 AboutQRCode.........................................4
2.3 QR Code Introduction................................5
2.3.1 Overview.........................................5
2.3.2 SymbolVersion....................................6
2.3.3 ModuleSize.......................................7
2.3.4 About Securing Margin............................7
2.4 QR Code Currently Application.......................7
2.5 Frequency-scanning Noise Distortion on Display......8
2.6 ProblemFormulation.................................10
2.7 System.............................................10
2.7.1 Hardware Development System.....................10
2.7.2 Software Development System.....................11
2.8 Challenges.........................................13
3 Digital Image Processing..............................14
3.1 MeanFiltering......................................14
3.2 GaussianFiltering..................................15
3.3 QR-code Denoisy Filtering..........................17
3.3.1 RGBToGray.......................................18
3.3.2 Mean filtering processing.......................18
3.3.3 Intensity Domain Gaussian filtering processing..19
3.4 BilateralFiltering.................................20
4 Experimental Results..................................23
4.1 Camera Resolution Analysis.........................25
4.2 Digital Image Filter Processing Analysis...........27
4.3 QR-code Denoisy Filter Processing Analysis.........30
4.4 Running Time of Denoisy Filter Processing Analysis.32
5 Conclusions and FutureWorks...........................33
dc.language.isoen
dc.subject照相機模組zh_TW
dc.subject嵌入式系統zh_TW
dc.subject影像處理zh_TW
dc.subject行動電話zh_TW
dc.subject影像濾波zh_TW
dc.subjectQR codezh_TW
dc.subjectimage filteringen
dc.subjectimage processingen
dc.subjectQR codeen
dc.subjectmobile phoneen
dc.subjectembedded systemen
dc.subjectcamera moduleen
dc.title在照相手機上QR-code影像雜訊之消除方法zh_TW
dc.titleA General Scheme for QR-code Image Denoising on the Camera Phoneen
dc.typeThesis
dc.date.schoolyear97-1
dc.description.degree碩士
dc.contributor.oralexamcommittee鄭振牟(Chen-Mou Cheng),黃俊龍(Jiun-Long Huang),楊得年(De-Nian Yang)
dc.subject.keyword影像處理,影像濾波,QR code,行動電話,嵌入式系統,照相機模組,zh_TW
dc.subject.keywordimage processing,image filtering,QR code,mobile phone,embedded system,camera module,en
dc.relation.page36
dc.rights.note有償授權
dc.date.accepted2009-02-03
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept電信工程學研究所zh_TW
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