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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93813完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁建均 | zh_TW |
| dc.contributor.advisor | Jian-Jiun Ding | en |
| dc.contributor.author | 葉明昌 | zh_TW |
| dc.contributor.author | Ming-Chang Ye | en |
| dc.date.accessioned | 2024-08-08T16:21:17Z | - |
| dc.date.available | 2024-08-09 | - |
| dc.date.copyright | 2024-08-08 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-30 | - |
| dc.identifier.citation | Directly mentioned in thesis
[1] https://en.wikipedia.org/wiki/Pinhole_camera_model [2] P. Viola and M. J. Jones, “Robust Real-Time Face Detection,” International Journal of Computer Vision, 57(2), pp. 137-154, 2004 [3] Ke-Jie Liao, “Face Detection by Outline, Color, and Facial Features,” M.S. Thesis, National Taiwan University, Taipei, Taiwan (ROC), June 2010 [4] H. Wu, Q. Chen, and M. Yachida, “Face Detection from Color Images Using a Fuzzy Pattern Matching Method,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 6, pp. 557-563, June 1999. [5] Tai Sing Lee, “Image representation using 2D Gabor wavelets,” IEEE Transactions on pattern analysis and machine intelligence, vol. 18, issue. 10, pp. 959-971, October 1996. [6] J. Illingworth and J. Kittler, “A survey of the Hough transform,” Computer vision, graphics, and image processing, vol. 44, issue. 1, pp. 87-116, October 1988 [7] Yu-Hsuan Tsai, “In-Plane and Out-of-Plane Color Face Detection,” M.S. Thesis, National Taiwan University, Taipei, Taiwan (ROC), June 2017 [8] E. Kaddouhi, Samir, A. Saaidi, and M. Abarkan. “A new robust face detection method based on corner points,” International Journal of Software Engineering and Its Applications, vol.8 No.11, pp. 25-40, 2014. [9] https://www.researchgate.net/figure/Example-of-2D-Gaussian-function-illustrates-an-example-of-2D-Gaussian-functions-The_fig4_296064445 [10] Zhiwei Zhu, Kikuo Fujimura, and Qiang Ji, “Real-time eye detection and tracking under various light conditions,” in Proceedings of the 2002 symposium on Eye tracking research & application, pp. 139-144, March, 2002 [11] Ji, Q., & Yang, X. “Real time visual cues extraction for monitoring driver vigilance,” In International Conference on Computer Vision Systems, pp. 107-124, Berlin, Heidelberg: Springer Berlin Heidelberg, June 2001. [12] M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt and B. Scholkopf, “Support vector machines,” in IEEE Intelligent Systems and their Applications, vol. 13, no. 4, pp. 18-28, July-Aug. 1998 [13] Greg Welch, and Gary Bishop, “An introduction to the Kalman filter,” September 1997. [14] https://www.researchgate.net/figure/Intel-R-RealSense-Depth-Camera-D435i_fig1_359522270 [15] Wijewickrema, Sudanthi NR, and Andrew P. Papliński. “Principal component analysis for the approximation of an image as an ellipse,” in The 13-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2005 in co-operation with EUROGRAPHICS, University of West Bohemia, Plzen, Czech Republic, pp. 69-70, 2005. [16] O. R. Vincent, and O. Folorunso, “A descriptive algorithm for sobel image edge detection,” in Proceedings of informing science & IT education conference (InSITE), vol. 40, pp. 97-107, 2009 [17] https://docs.opencv.org/4.7.0/d3/dc0/group__imgproc__shape.html [18] “Camera Matrix,” class notes for 16-385 Computer Vision, The Robotics Institute, Carnegie Mellon University, Spring 2017. [19] https://www.fdxlabs.com/calculate-x-y-z-real-world-coordinates-from-a-single-camera-using-opencv/ [20] Bo-Nan Jiang, “On the least-squares method,” Computer methods in applied mechanics and engineering, vol. 152, issue. 1-2, pp. 239-257, January 1998. Other references [21] Bernd Jähne, Horst Haußecker, and Peter Geißler, Handbook of computer vision and applications, vol.2, San Diego: Academic Press, 1999. [22] Bernd Jähne, Digital Image Processing, Springer Science & Business Media, 2005. [23] Jonathon Shlens, “A Tutorial on Principal Component Analysis”, arXiv: 1404.1100, April 2014 [24] Svante Wold, et al., “Principal component analysis”, Chemometrics and Intelligent Laboratory Systems, Volume 2, Issues 1–3, August 1987, Pages 37-52 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93813 | - |
| dc.description.abstract | 本論文將呈現一種快速的眼球追蹤演算法，該演算法將接收影像的深度資訊以及灰階紅外線資訊，再透過傳統的影像處理演算法，以快速(每秒可處理超過90張圖片)又不失精確的方式，從這張含有一張人臉的圖片中，定位出眼球在以相機為原點之現實座標系中的位置。除了呈現結果之外，本論文也會詳加說明這種演算法的限制與優缺點。
大體而言，本方法利用了深度資訊來快速定位出臉部的位置，進而縮小尋找眼球的範圍，以同時達到高速執行以及降低錯誤率的目的。在定位出了臉部位置之後，出於速度考量，本方法將繼續使用運算較不複雜的影像處理演算法，在臉部的圖片中定位出眼球在圖片中的位置，最後以針孔相機模型[1]，將圖片上的像素座標轉換為現實座標。 | zh_TW |
| dc.description.abstract | In this thesis, we will purpose a fast eye-tracking method which takes depth image and gray-scale IR image as input, and put them into a traditional-image-processing-algorithm- based system. Given an IR image which containing exactly one face and the corresponding depth image, the purposed method can locate the real-world coordinate which the camera is at origin with high speed (>90 frames per second) with acceptable error.
Basically, this method takes advantage of the depth information to quickly locate face, which can shrink the range we detect the eyeballs and then decrease the error rate and accelerate the operation. After finding the face region, in need of high execution speed, we apply some less complicated computer vision algorithms to locate the eyeball’s position and finally transform the pixel coordinate on the image to the real-world coordinate though pinhole camera model[1]. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-08T16:21:17Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-08T16:21:17Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Overview 1 1.2 Organization 4 Chapter 2 Related Works 5 2.1 Face Detection Algorithm 5 2.2 Eye Detection in Colored Image 7 2.3 Eye Detection in IR image 11 Chapter 3 Proposed Method 13 3.1 Camera and Input Images 13 3.1.1 Camera 13 3.1.2 Depth Information and Simple Pinhole Camera Model 14 3.1.3 Input Images 16 3.2 Preprocessing 17 3.2.1 Goals of Preprocessing 17 3.2.2 Flow of Preprocessing 19 3.2.3 Performance of Preprocessing 23 3.3 States of Face 24 3.3.1 Roll 25 3.3.2 Pitch up, Yaw and Mask 27 3.3.3 Pitch Down 35 3.4 Detection and Postprocessing 37 3.4.1 Detection 37 3.4.2 Pick A Pair of Eyes 43 3.4.3 Postprocessing 46 3.5 Coordinate Transformation and Correction 47 3.5.1 Recovery and Coordinate Transformation 47 3.5.2 Oscillations and Correction 49 Chapter 4 Experiment Result 54 4.1 Detection Rates in Different Cases 54 4.2 Accuracy, Precision, and Execution Time 57 4.2.1 Experiment Setup 57 4.2.2 Scenarios of the Experiment 59 4.2.3 Result 59 4.3 Performance for a Moving Object 67 Chapter 5 Conclusion and Future Work 73 5.1 Conclusion 73 5.2 Future Work 73 REFERENCE 75 | - |
| dc.language.iso | en | - |
| dc.subject | 深度影像 | zh_TW |
| dc.subject | 眼球追蹤 | zh_TW |
| dc.subject | 電腦視覺 | zh_TW |
| dc.subject | 數位影像處理 | zh_TW |
| dc.subject | 快速 | zh_TW |
| dc.subject | Eye tracking | en |
| dc.subject | depth images | en |
| dc.subject | computer vision | en |
| dc.subject | digital image processing | en |
| dc.subject | rapid | en |
| dc.title | 基於影像處理及藉由深度資訊輔助的快速眼球追蹤算法 | zh_TW |
| dc.title | Depth Information Assisted Image Processing Based Rapid Eye-Tracking Method | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 盧奕璋;王鵬華;歐陽良昱 | zh_TW |
| dc.contributor.oralexamcommittee | Yi-Chang Lu;Peng-Hua Wang;Liang-Yu Ou Yang | en |
| dc.subject.keyword | 眼球追蹤,深度影像,快速,數位影像處理,電腦視覺, | zh_TW |
| dc.subject.keyword | Eye tracking,depth images,rapid,digital image processing,computer vision, | en |
| dc.relation.page | 77 | - |
| dc.identifier.doi | 10.6342/NTU202402363 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-01 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf | 2.72 MB | Adobe PDF | 檢視/開啟 |
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