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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭彥甫(Yan-Fu Kuo) | |
| dc.contributor.author | Tzu-Han Chou | en |
| dc.contributor.author | 周子涵 | zh_TW |
| dc.date.accessioned | 2021-05-14T17:43:43Z | - |
| dc.date.available | 2020-08-10 | |
| dc.date.available | 2021-05-14T17:43:43Z | - |
| dc.date.copyright | 2015-08-10 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-08-05 | |
| dc.identifier.citation | 1. Hubel, D.H. and T.N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of physiology, 1962. 160(1): p. 106.
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Dong, H., et al. 2D barcode image binarization based on wavelet analysis and Otsu's method. in International Conference on Computer Application and System Modeling. 2010. Taiyuan: IEEE. p. 30-33. 8. Wei, X. and S. McCloskey. 2D Barcode localization and motion deblurring using a flutter shutter camera. in IEEE Workshop on Applications of Computer Vision. 2011. p. 159-165. 9. Ohbuchi, E., H. Hanaizumi, and L.A. Hock. Barcode readers using the camera device in mobile phones. in International Conference on Cyberworlds. 2004. p. 260-265. 10. Lin, J.-A. and C.-S. Fuh, 2D Barcode Image Decoding. Mathematical Problems in Engineering, 2013. 2013: p. 10. 11. Leong, L.K. and W. Yue, Extraction of 2D barcode using keypoint selection and line detection, in Advances in Multimedia Information Processing. 2009, Springer. p. 826-835. 12. Jain, A.K. and Y. Chen. 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ACM, 1972. 15(1): p. 11-15. 29. Bradley, D. and G. Roth, Adaptive Thresholding using the Integral Image. Journal of Graphics, GPU, and Game Tools, 2007. 12(2): p. 13-21. 30. Ohbuchi, E., H. Hanaizumi, and L.A. Hock. Barcode readers using the camera device in mobile phones. in Cyberworlds, 2004 International Conference on. 2004. p. 260-265. 31. Gonzalez, R.C. and R.E. Woods, Digital Image Processing (3rd Edition). 2006: Prentice-Hall, Inc. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/4584 | - |
| dc.description.abstract | 條碼長期被當作資訊的圖形辨識原件，不過在複雜背景中，可自動偵測出不同扭曲或傾斜的條碼，還是一大挑戰。此研究提出可自動偵測這些類型的條碼定位系統。在這研究中用來測試此系統的條碼，包含一維條碼Code 39、Code 128和EAN- 13，與二維條碼QR code。此定位系統利用捲積類神經網路(Convolutional neural network)演算法，辨別影像中條碼的區域。接著透過影像處理的方法，將區域中的條碼切取出來。實驗結果證實此條碼定位系統是可以偵測特定範圍的條碼大小，甚至對於模糊或變形的條碼也能有效的偵測能力。此演算法在449張實驗影像中，可以達到86.25%的偵測率與78.55%切取率。 | zh_TW |
| dc.description.abstract | Barcodes have been long used for data storage. Locating barcodes in images of complex background is an essential yet challenging step for automatic barcode reading. This study aimed to detect and to extract one-dimensional Code 39, Code 128, and EAN-13 barcodes and two-dimensional QR barcodes in images of arbitrary backgrounds. The proposed method involved a convolutional neural network for detecting parts of barcodes. Once positive detection was confirmed, image processing algorithms were implemented to extract barcodes from the image. Experiments demonstrated that the proposed approach was able to locate barcodes of various module sizes and was robust to blurring, rotation, and deformation. The approach achieved an overall detection rate of 86.45% and an extraction rate of 78.55% using a set of 449 images. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-14T17:43:43Z (GMT). No. of bitstreams: 1 ntu-104-R02631023-1.pdf: 1427775 bytes, checksum: 37b70124adb555536bb92d302e490a84 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | ACKNOWLEDGEMENTS i
摘要 ii ABSTRACT iii TABLE OF CONTENTS iv LIST OF TABLES vi LIST OF FIGURES vii CHAPTER 1. INTRODUCTION 1 1.1 Barcode 1 1.2 Convolutional neural networks 2 1.3 Objectives 2 1.4 Organization 3 CHAPTER 2. LITERATURE REVIEW 4 2.1 One-dimensional barcode localization 4 2.2 Two-dimensional barcode localization 4 2.3 Detecting barcodes using texture features 5 2.4 Convolution neural network 5 CHAPTER 3. MATERIAL AND METHODS 7 3.1 Collection of training image patches 7 3.2 CNN architecture 9 3.3 Detection of barcode with various module sizes 10 3.4 Scan line extraction for one-dimensional barcodes 11 3.5 Region extraction for two-dimensional barcodes 13 CHAPTER 4. RESULTS AND DISCUSSION 15 4.1 Feature maps of the trained CNN model 15 4.2 Robustness of the CNN classifier to blur, module size variation, and rotation 16 4.3 The process time of the proposed barcode detection 19 4.4 The results of barcode detection on the test data 20 4.5 The results of barcode extraction on the test data 22 CHAPTER 5. CONCLUSION 24 REFERENCES 25 | |
| dc.language.iso | en | |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 條碼定位 | zh_TW |
| dc.subject | 捲積類神經網路 | zh_TW |
| dc.subject | 影像處理 | zh_TW |
| dc.subject | barcode localization | en |
| dc.subject | machine learning | en |
| dc.subject | image processing | en |
| dc.subject | convolutional neural network | en |
| dc.title | 利用捲積類神經網路定位複雜背景中的條碼 | zh_TW |
| dc.title | Barcode Localization Using Convolutional Neural Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 花凱龍(Kai-Lung Hua),林顯易(Hsien-I Lin) | |
| dc.subject.keyword | 條碼定位,捲積類神經網路,影像處理,機器學習, | zh_TW |
| dc.subject.keyword | barcode localization,convolutional neural network,image processing,machine learning, | en |
| dc.relation.page | 28 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2015-08-06 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物機電工程學系 | |
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