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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 曹恆偉 | |
dc.contributor.author | Ming-Ying Tsai | en |
dc.contributor.author | 蔡銘穎 | zh_TW |
dc.date.accessioned | 2021-06-16T09:20:13Z | - |
dc.date.available | 2022-07-07 | |
dc.date.copyright | 2017-07-07 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-07-03 | |
dc.identifier.citation | [1]Tang, Yuan Yan. Wavelet theory and its application to pattern recognition. Vol. 36. World Scientific, 2000.
[2]M. I. Malik, M. Liwicki, L. Alewijnse, W. Ohyama, M. Blumenstein and B. Found, 'ICDAR 2013 Competitions on Signature Verification and Writer Identification for On- and Offline Skilled Forgeries (SigWiComp 2013),' 2013 12th International Conference on Document Analysis and Recognition, Washington, DC, 2013, pp. 1477-1483. [3]Turk, Matthew, and Alex Pentland. 'Eigenfaces for recognition.' Journal of cognitive neuroscience 3.1 (1991): 71-86. [4]C. M. Bishop, Pattern recognition and machine learning, Springer, 2006. [5]L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement learning: a survey,” J. Artif. Intell. Res. 4, pp. 237-285, 1996. [6]Wei-Lun Chao, “Integrated Machine Learning Algorithms for Human Age Estimation ” Graduate Institute of Electronics Engineering College of Electrical Engineering and Computer Science, National Taiwan University, 2011 [7]Wikipedia,“Decision_tree” https://zh.wikipedia.org/wiki/%E5%86%B3%E7%AD%96%E6%A0%91 [8]http://sjchen.im.nuu.edu.tw/MachineLearning/final/CLS_DT.pdf [9]Breiman, Leo. 'Random forests.' Machine learning 45.1 (2001): 5-32. [10]Yi Lu, “Currency Serial Number Recognition” Graduate Institute of Computer Science and Information Engineering, National Taiwan University, 2015. [11]F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, Vol. 12, pp. 2825-2830, 2011. [12]Kovari, Bence, and Hassan Charaf. 'A study on the consistency and significance of local features in off-line signature verification.' Pattern Recognition Letters 34.3 (2013): 247-255. [13]Rajpoot, Kashif M., and Nasir M. Rajpoot. 'Wavelets and support vector machines for texture classification.' Multitopic Conference, 2004. Proceedings of INMIC 2004. 8th International. IEEE, 2004. [14]Zhang, Bai-Ling, Haihong Zhang, and Shuzhi Sam Ge. 'Face recognition by applying wavelet subband representation and kernel associative memory.' IEEE Transactions on neural networks 15.1 (2004): 166-177. [15]J. J. Ding, “Time Frequency Analysis and Wavelet Transform”, available in http://disp.ee.ntu.edu.tw/tutorial.php [16]Ferrer, Miguel A., et al. 'Robustness of offline signature verification based on gray level features.' IEEE Transactions on Information Forensics and Security 7.3 (2012): 966-977. [17]Deng, Peter Shaohua, et al. 'Wavelet-based off-line handwritten signature verification.' Computer vision and image understanding 76.3 (1999): 173-190. [18]A. Hassane, S. Al-Maadeed, and A. Bouridane, “A set of geometrical features for writer identification,” Neural Information Processing.Springer Berlin Heidelberg, 2012. [19]M. B. Yilmaz, B. Yanikoglu, C. Tirkaz, and A. Kholmatov, “Offline signature verification using classifier combination of hog and lbp features,” [20]Nguyen, Cuong, Yong Wang, and Ha Nam Nguyen. 'Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic.' (2013). [21]Bernard, Simon, Sébastien Adam, and Laurent Heutte. 'Using random forests for handwritten digit recognition.' Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on. Vol. 2. IEEE, 2007. [22]Fanelli, Gabriele, et al. 'Random forests for real time 3d face analysis.' International Journal of Computer Vision 101.3 (2013): 437-458. [23]Wikipedia, “Supervised Learning” https://en.wikipedia.org/wiki/Supervised_learning [24]Sanmorino, Ahmad, and Setiadi Yazid. 'A survey for handwritten signature verification.' Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on. IEEE, 2012. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59313 | - |
dc.description.abstract | 過去數十年來,手寫簽名辨識被廣泛且深入的研究,其應用包括金融業、
信用卡驗證、保全系統等等。一般來說,手寫辨識系統被分為兩個部分—線上 辨識以及離線辨識,線上辨識需要觸控筆及平板電腦等工具擷取動態手寫資訊, 而離線辨識則透過掃描器將手寫資訊轉換成圖片檔等靜態格式。 由於在本國,司法機構在針對偽造簽名辨識的問題上,並未有一套科學系統上的驗證,仍是以人眼透過視覺觀察簽名的各種資訊,諸如筆順、力道、弧度等來判斷是否為偽造簽名。本論文透過近年來快速進步的電腦視覺以及機器學習技術,提出一套系統化,並且可靠的方式,可在少量的資料量下,成功地將簽名分辨出是否為偽造簽名。 本論文首章為論文簡介,而第二章以及第三章為背景知識介紹,從第四章開始為本篇論文的主要貢獻,也就是系統架構設計,包含簽名影像的前處理、轉換、分類、測試等,並在第五章提供不同參數、不同模型的模擬比較,末章則為結語與未來展望。 | zh_TW |
dc.description.abstract | In the past couple of decades, techniques for handwritten signature verification have been thoroughly studied and put into practice in various systems, which include security systems and financial field where credit cards verification is much needed. Generally speaking, handwritten signature verification system can be categorized into two kinds, online and offline verification. The former requires stylus pens and tablet computers to capture dynamic signature information, whilst for offline verification, a scanner is used to turn handwritten information into static formats such as image files.
Due to the fact that there is not yet a scientific system for signatures verification upon forgery problems in the Taiwan judicial system, the procedure is mainly carried out through trained human eyes, with which validate the signatures with different unique cursive writing styles. In this thesis, a more systematic and dependable method on signatures verification is proposed. With the rapid development of computer vision and machine learning, it is possible to identify whether the signature is forged even under limited data. The layout of this thesis is as follows: The first three chapters give a brief introduction and provide background knowledge in the field of handwritten signature verification. The fourth chapter focuses on system architecture design, including data preprocessing, transformation, classification, and testing. Chapter five present the comparison with different parameters and simulation models. And finally, the last chapter wraps up the thesis with discussion and future work. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T09:20:13Z (GMT). No. of bitstreams: 1 ntu-106-R04942131-1.pdf: 2438031 bytes, checksum: bf0288f1eb6c93a3098aa48facda14ef (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書 i
中文摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 viii 第一章 緒論 1 1.1 前言 1 1.2 研究動機 1 1.3 論文架構 2 第二章 小波轉換概述 3 2.1 小波轉換簡介 3 2.1.1 連續小波轉換 4 2.1.2 離散小波轉換 5 2.1.3 二維離散小波轉換 6 2.2 哈爾小波轉換 8 第三章 機器學習簡介 13 3.1 資料集 14 3.1.1 資料集表示法 14 3.1.2 訓練集與測試集 15 3.2 機器學習種類 16 3.2.1 監督式學習 ( Supervised learning ) 16 3.2.2 非監督式學習 ( Unsupervised learning ) 18 3.2.3 半監督式學習 ( Semi-supervised learning ) 20 3.2.4 強化式學習 ( Reinforcement learning ) 21 3.3 機器學習架構 22 3.4 隨機森林 27 3.4.1 決策樹 27 3.4.2 隨機森林 29 第四章 手寫簽名辨識系統設計 31 4.1 樣本前處理 32 4.1.1 樣本集 33 4.1.2 前處理 37 4.2 特徵擷取 39 4.3 分類器 43 第五章 系統模擬結果 46 5.1 模擬正確性之驗證 46 5.2 模擬結果與比較 47 第六章 結論與未來展望 51 6.1 結論 51 6.2 未來展望 52 參考文獻 53 | |
dc.language.iso | zh-TW | |
dc.title | 基於小波轉換與機器學習的簽名真偽辨識系統 | zh_TW |
dc.title | Handwritten Signature Verification System
Based on Wavelet Transform and Machine Learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 范育成,李揚漢,丁建均,詹原豪 | |
dc.subject.keyword | 簽名辨識,手寫辨識,特徵擷取,電腦視覺,機器學習, | zh_TW |
dc.subject.keyword | signature recognition,handwritten recognition,feature extraction,computer vision,machine learning, | en |
dc.relation.page | 56 | |
dc.identifier.doi | 10.6342/NTU201701300 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-07-04 | |
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 |
顯示於系所單位: | 電信工程學研究所 |
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