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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 顏嗣鈞(Hsu-chun Yen) | |
| dc.contributor.author | Hao-Yu Wang | en |
| dc.contributor.author | 王浩宇 | zh_TW |
| dc.date.accessioned | 2021-06-16T06:45:11Z | - |
| dc.date.available | 2019-08-01 | |
| dc.date.copyright | 2014-08-01 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-07-28 | |
| dc.identifier.citation | [1] J. P. Wachs, M. Kolsch, H. Stern, and Y. Edan, “Vision-based Hand-gesture Applications,” Communications of the ACM, vol. 54, pp. 60-71, 2011.
[2] S. Mitra and T. Acharya, “Gesture Recognition: A Survey,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 37, pp. 311-324, 2007. [3] A. Erol, G Bebis, M. Nicolescu, R. D. Boyle, and X. Twombly, “Vision-based Hand Pose Estimation: A Review,” Computer Vision and Image Understanding, vol. 108, pp. 52-73, 2007. [4] G. R. S. Murthy and R. S. Jadon, “A Review of Vision Based Hand Gesture Recognition,” International Journal of Information Technology and Knowledge Management, vol. 2, pp. 405-410, 2009. [5] Kinect for Windows SDK. Available: http://www.microsoft.com/en-us/kinectforwindowsdev/default.aspx [6] T. T. Chu, and C. Y. Su, “A Kinect-based Handwritten Digit Recognition for TV Remote Controller,” in Proc. IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 414-419, 2012. [7] F. A. Huang, C. Y. Su, and T. T. Chu, “Kinect-based Mid-air Handwritten Digit Recognition using Multiple Segments and Scaled Coding,” in Intelligent Signal Processing and Communications Systems (ISPACS), pp. 694-697, 2013. [8] X. Zhang, Z. Ye, L. Jin, Z. Feng, and S. Xu, “A New Writing Experience: Finger Writing in the Air Using a Kinect Sensor,” in IEEE Multimedia, vol. 20, pp. 85-93, 2013. [9] J. Shotton, et al., “Real-time Human Pose Recognition in Parts from Single Depth Images,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1297-1304, 2011. [10] J. Kim, N. Thang and T. Kim, “3-D Hand Motion Tracking and Gesture Recognition Using a Data Glove,” in IEEE International Symposium on Industrial Electronics, pp. 1013– 1018, 2009. [11] P. Viola and M. Jones, “Robust Real-time Face Detection,” International Journal of Computer Vision, vol. 57, May. 2004, pp. 137-157. [12] Q. Chen, N. Georganas, and E. Petriu, “Real-time Vision-based Hand Gesture Recognition Using Haar-like Feature,” in Proc. Instrumentation and Meaturement Technology Conference, pp. 1-6, 2007. [13] D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, Nov. 2004, pp. 91-110. [14] C. Wang and K. Wang, “Hand Posture Recognition Using Adaboost with SIFT for Human Robot Interaction,” Recent Progress in Robotics: Viable Robotic Service to Human, 2009, pp. 317-329. [15] J. Suarez, and R. R. Murphy, “Hand Gesture Recognition with Depth Images: A Review,” In Proc. of Int. Sym. on Robot and Human Interactive Communication, pp. 411-417, 2012. [16] Y. Li, 'Hand Gesture Recognition Using Kinect,' in Software Engineering and Service Science (ICSESS), pp. 196-199, 2012. [17] C. Keskin, F. Kirac, Y. E. Kara, and L. Akarun, 'Real Time Hand Pose Estimation Using Depth Sensors,' in Computer Vision Workshops (ICCV Workshops), pp. 1228-1234, 2011. [18] J. L. Raheja, A. Chaudhary, and K. Singal, “Tracking of Fingertips and Centers of Palm Using KINECT,” in Computational Intelligence, Modelling and Simulation (CIMSiM), pp. 248-252, 2011. [19] M. Van den Bergh, D. Carton, R. De Nijs, N. Mitsou, C. Landsiedel, K. Kuehnlenz, D. Wollherr, L. Van Gool, and M. Buss, “Real-time 3D Hand Gesture Interaction with a Robot for Understanding Directions from Humans,” in RO-MAN, pp. 357-362, 2011. [20] Z. Zafrulla, H. Brashear, T. Starner, H. Hamilton, and P. Presti, “American Sign Language Recognition with the Kinect,” in International Conference on Multimodal Interfaces, pp. 279-286, 2011. [21] P. Breuer, C. Eckes, and S. Miller, “Hand Gesture Recognition with a Novel IR Time-of-Flight Range Camera–A Pilot Study,” in Mirage (Computer Vision/Computer Graphics Collaboration Techniques), pp. 247-260, 2007. [22] M. Van den Bergh and L. Van Gool, ” Combining RGB and ToF Cameras for Real-time 3D Hand Gesture Interaction,” in Workshop on Applications of Computer Vision (WACV), pp. 66-72, 2011. [23] N. Jojic, B. Brumitt, B. Meyers, S. Harris, and T. Huang, “Detection and Estimation of Pointing Gestures in Dense Disparity Maps,” in Automatic Face and Gesture Recognition, pp. 468-475, 2000. [24] D. McNeill, Language and gesture vol. 2: Cambridge Univ Pr, 2000. [25] Z. Ren, J. Yuan, J. Meng, and Z. Zhang, “Robust Part-based Hand Gesture Recognition using Kinect Sensor,” IEEE Trans. Multimedia, vol. 15, pp. 1110–1120, 2013. [26] Y. Wang, C. Yang, X. Wu, S. Xu, and H. Li, “Kinect Based Dynamic Hand Gesture Recognition Algorithm Research,” in Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp. 274 – 279, 2012. [27] C. Cortes and V. Vapnik, “Support-vector Network”, Machine Learning, 20, pp. 273-297, 1995. [28] W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, “Section 16.5. Support Vector Machines”. Numerical Recipes: The Art of Scientific Computing (3rd ed.). [29] Weka. Available: http://www.cs.waikato.ac.nz/ml/weka/ | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57415 | - |
| dc.description.abstract | 人機互動在近年來是十分熱門的研究領域，而手勢辨識提供了更自然的溝通方式，是人機互動相當重要的部份。手寫辨識也是屬於手勢辨識的一部份，它提供了人機互動中輸入字母的替代方案。在本篇論文中，我們做出手寫辨識系統，讓使用者不需要傳統的輸入設備像是鍵盤和滑鼠就能夠輸入數字及英文字母。
在人機互動中對於手寫數字和英文字母需要即時的辨識並且對準確率要求比較高。因此為了提升辨識的準確率，我們提出了一種新的特徵演算法，此特徵演算法包含了書寫軌跡的時間性和空間性，並經由支援向量機和隨機決策森林進行辨識。最後我們也透過實驗證明本篇論文所提出的方法能夠即時的辨識手寫數字及英文字母並擁有相當高的精準度。 | zh_TW |
| dc.description.abstract | Human-computer interaction (HCI) has been a popular research field recently. Hand gesture recognition is an important part of HCI that provides a natural way of communication. Handwritten recognition is a part of hand gesture recognition that provides an alternative method to input characters. In this thesis, we propose a handwritten recognition system to input English characters and digits without using traditional input devices such as keyboards and mice.
Accuracy and real time processing are highly desired in the handwritten digit and character recognition of HCI. In order to improve the accuracy, we suggest a new feature extracting algorithm which contains the temporal and spatial information of hand writing paths. Furthermore, we use support vector machines and random forests to carry out feature classification. Experimental results show that the proposed method has a very high accuracy in the handwritten digit and character recognition in real time. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T06:45:11Z (GMT). No. of bitstreams: 1 ntu-103-R01921075-1.pdf: 875712 bytes, checksum: 73ac1ac2eee74356b3958c9aed2bed4d (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 vii Chapter 1 緒論 1 1.1 研究背景與動機 1 1.2 系統簡介 2 1.3 論文架構 3 Chapter 2 相關研究 4 2.1 模式識別簡介 4 2.2 手部定位 5 2.3 深度感測器 6 2.4 靜態手勢辨識 7 2.5 動態手勢辨識 8 Chapter 3 系統架構與實作 9 3.1 系統架構 9 3.2 Kinect 骨架追蹤 9 3.2.1 身體部位標籤 10 3.2.2 深度影像特徵 11 3.2.3 隨機決策森林 12 3.2.4 定位關節位置 13 3.3 特徵擷取 15 3.4 手寫辨識 19 3.4.1 隨機決策森林 19 3.4.2 支援向量機 19 Chapter 4 實驗結果與分析 23 4.1 環境設定 23 4.2 數字辨識 24 4.3 英文字母辨識 25 4.4 數字及英文字母辨識 30 4.5 系統效能分析 33 Chapter 5 結論與未來發展 36 REFERENCES 38 | |
| dc.language.iso | zh-TW | |
| dc.subject | 手寫數字辨識 | zh_TW |
| dc.subject | 模式識別 | zh_TW |
| dc.subject | 手寫英文字母辨識 | zh_TW |
| dc.subject | 動態手勢辨識 | zh_TW |
| dc.subject | pattern recognition | en |
| dc.subject | handwritten digit recognition | en |
| dc.subject | handwritten character recognition | en |
| dc.subject | dynamic hand gesture recognition | en |
| dc.title | 利用Kinect之手寫英文字母及數字辨識 | zh_TW |
| dc.title | Handwritten English Character and Digit Recognition Using Kinect | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭斯彥(Sy-Yen Kuo),雷欽隆(Chin-Laung Lei),莊仁輝(Jen-Hui Chuang),黃秋煌(Chiu-Huang Huang) | |
| dc.subject.keyword | 模式識別,手寫數字辨識,手寫英文字母辨識,動態手勢辨識, | zh_TW |
| dc.subject.keyword | pattern recognition,handwritten digit recognition,handwritten character recognition,dynamic hand gesture recognition, | en |
| dc.relation.page | 41 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-07-28 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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