閉眼狀態下眼睛的移動偵測系統應用於監測眼睛狀態之眼罩

簡易檢索 / 詳目顯示

回結果列表

研究生：	甘志雯 Chih-Wen Kan
論文名稱：	閉眼狀態下眼睛的移動偵測系統應用於監測眼睛狀態之眼罩 An Image-Based Closed-Eye Motion Detection System Applicable to a Monitoring Eyemask
指導教授：	鐘太郎 Tai-Lang Jong
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2005
畢業學年度：	93
語文別：	英文
論文頁數：	54
中文關鍵詞：	眼罩、眼睛移動偵測
外文關鍵詞：	eyemask, eye-tracking
相關次數：	點閱：1 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

在本篇論文中，我們利用數位影像處理的方式，發展出一套能夠偵測閉眼狀態下眼睛移動的演算法。此系統在讀進閉眼影像後，能夠自動判斷出眼睛是否正在移動。在前處理步驟中，我們將讀進的影像判斷出睫毛的位置，進一步定位眼睛可能移動的範圍，此範圍稱之為ROI。我們再將ROI做特徵抽取，分別抽取三種特徵：空間特徵(Spatial Domain Features)、統計特徵(Statistical Features)、頻率特徵(Frequency Domain Features)。為了判斷出眼睛是否移動，將這三種特徵及他們的各式組合分別用類神經網路中的Support Vector Machine演算法來進行眼睛移動狀態的判讀。我們分析並統計了採用三種特徵及各式組合的判讀正確率，並進行比較。此外，在實驗中，我們也嘗試了兩種不同的判讀模式，分別為Leave-One-Out及Leave-One-Person-Out模式。兩種模式的差異是：前者是使用受測者自己的影像來做類神經網路的訓練，而後者則是使用系統內建的影像資料庫來判斷受測者的眼睛影像。實驗的結果顯示，在最好的情況下，Leave-One-Out模式用在受測者D，可以得到95.5%的正確率，而Leave-One-Person-Out模式用在受測者G，可以得到96.0%的正確率。實驗的正確率令人滿意，此方法應可對於睡眠監測的臨床研究做出貢獻。

This thesis focuses on developing an algorithm to the detection of the motion of closed-eye using image processing methods. The designed system is able to determine whether the input frame of eye image is a frame with eye-motion or without it. In the procedure, first a preprocessing stage is performed. The preprocessing step is to confine the input image to a certain region of interest including only the eye area. Second, the region of interest is used to extract three different types of features: spatial domain features, statistical features, and frequency domain features. These features are adopted to make decisions of the moving or freezing state of the eye, by a classifier of the Support Vector Machine (SVM) of Neural Networks. The effectiveness of the three types of features and their combinations are analyzed and the correct rates are compared. In addition, two different methods of experiments are attempted: the leave-one-out and the leave-one-person-out methods. The former is to use the prior knowledge of the examinee himself as the training data, while the latter is to use the database collect by us to examine a new examinee’s eye motion. The best correct rate for the leave-one-out method is 95.5% using the spatial features for examinee D, while the best correct rate for the leave-one-person-out method is 96.0% using the spatial and statistical features for examinee G. The results are quite satisfactory and can make contributions to the sleeping monitoring clinical research.

Abstract ...............................................I
Contents .............................................III
List of Figures.........................................V
List of Tables.........................................VI
Chapter 1 Introduction .................................1
1.1    Introduction ..................................1
1.2    Literature Review .............................1
1.2.1    Physiology Background of Sleep ................1
1.2.2    Sleep Monitoring Equipments ...................4
1.2.3    Eye-Tracking Methods ..........................5
1.3    System Objective ..............................6
1.4    Thesis Objective ..............................6
1.5    Thesis Overview ...............................7
Chapter 2 Preprocessing ................................8
2.1 Image Constraints ..................................8
2.2 Locating Region of Interest ........................9
        2.2.1 Eyelashes Locating .............9
2.2.1.1 Finding pixels with the lowest gray-level.......10
        2.2.1.2 Morphological largest-area search..................................................11
    2.2.2 Region of Interest.......................13
Chapter 3 Feature Extraction and the SVM Classifier.....14
3.1 Spatial Domain Features.............................14
    3.1.1 Projected Sum Searching Method...........16
    3.1.2 Contouring Method........................17
        3.1.2.1 Smoothing.....................18
        3.1.2.2 Specify Spacing of Contour ...19
        3.1.2.3 Erosion ......................20
        3.1.2.4 The Center of Mass............22
    3.1.3 The Difference Vectors ..................24
3.2 Statistical Features ...............................25
    3.2.1 The Frame Difference Image ..............26
    3.2.2 Probability Density Function of the Eye..26
    3.2.3 Segmentation and Gaussian Lowpass Filter.27
    3.2.4 The Energy Feature ......................29
    3.2.5 The Gray-Level Range Feature ............30
3.3 Frequency Domain Features ..........................30
    3.3.1 Gabor Filter ............................32
    3.3.2 Discrete Cosine Transform and Frequency Components..............................................31
3.4 The Classifier – The Support Vector Machine of Neural Network.................................................35
    3.4.1 Neural Network ..........................35
    3.4.2 Support Vector Machine ..................36
Chapter 4 Experimental Results .........................40
    4.1 Leave-One-Out Results for Each Examinee....41
    4.2 Leave-One-Person-Out Results...............45
Chapter 5 Conclusion and Future Work ...................47
    5.1 Conclusion ................................47
    5.2 Future Work ...............................49
Reference ..............................................51

                                

[1] N. R. Carlson, Physiology of Behavior 8th Edition, Allyn and Bacon, 2003.

[2] H. Berger, “Ueber das Elektroenzephalogramm des Menschen,” Arch. Psychiatr. Nervenkr, 1929.

[3] 鄭智銘 “睡眠監測相關研究簡介” Jul., 2002.

[4] U. Anliker, J. A. Ward, P. Lukowicz, G. Troester, F. Dolveck, M. Baer, F. Keita, E. B. Schenker, F. Catarsi, L. Coluccini, A. Belardinelli, D. Shklarski, M. Alon, E. Hirt, R. Schmid, and M. Vuskovic, “AMON: A Wearable Multiparameter Medical Monitoring and Alert System” IEEE Trans. on Info. Tech. in Biomedicine, Vol. 8, No.4, Dec. 2004.

[5] K.P. White, T.E. Hutchinson, and J.M. Carley, “Spatially Dynamic Calibration of an Eye-Tracking System,” IEEE Trans. on Systems, Man, and Cybernetics, Vol. 23, No. 4, pp. 1162-1168, 1993.

[6] M. Eriksson, N. P. Papanikolopoulos, “Eye-tracking for Detection of Driver Fatigue,” IEEE Conference on Intelligent Transportation System, 1997. ITSC 97.

[7] Bin.Chen, Zhi-Qiang Liu, Xiang Hua Zhu, “Eye Location in Human Face Location Using Fuzzy Integral,” ieee cnf, Volume 4, 2-5 Page(s):2500 - 2502, Nov. 2003.

[8] Fang-Chung Yang, Chung-Hsien Kuo, Ming-Yuan Tsia, Shiao-Chun Huang, “Image-Based Sleep Motion Recognition Using Artificial Neural Network,” ieee cnf Volume 5, 2-5 Page(s):2775 – 2780, Nov. 2003.

[9] K. Sung, M.F. Reschke, “A Model-Based Approach for the Measurement of Eye Movements Using Image Processing,” Technical Paper 3680, NASA, Apr. 1997.

[10] T. Ashit, M. John_Michael, M. Steve, L. Raymond, L. Clayton, and L.L. James, “Eye–tracking architecture for biometrics and remote monitoring,” Applied Optics, Vol.44, No.5, pp 693-700, Feb., 2005.

[11] G. M. Hatzilabron, N. Greenberg , R. J. Sclabassi, T. Carroll, R. D. Guthrie, and Mark S. Scher,”A Comparison of Conventional and Matched Filtering Techniques for Rapid Eye Movement Detection of the Newborn,” ieee transaction on biomedical engineering, Vol. 41, NO. 10, pp 990-995, Oct., 1994.

[12] Q. R. Chen, W. K. Cham and H. T. Tsui, “A Method for Estimation and Accurately Extracting the Eyebrow in Human Face Image,” Proceedings. Ieee 2002 International Conference on Volume 3, Page(s):793 – 796 vol.3, June 2002.

[13] K. Sung, and M. F. Reschke, “A Model-Based Approach for the Measurement of Eye Movements Using Image Processing,” nasa, Technical Paper 3680, April 1997.

[14] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd edition, Prentice Hall, 2002.

[15] Sheldon Ross, A First Course in Probability 4th edition, Addison-Wesley, 2001, pp. 361, Ex 6h.

[16] S. Haykin, Neural Networks, 2nd edition, Prentice Hall, 1999.

[17] D. Vignon, B.C. Lovell, and R.J. Andrews, “General Purpose Real-Time Object Tracking Using Hausdorff Transforms,” IPMU2002: Special Session on Intelligent Systems for Video Processing, 2002.

[18] I. Ravyse, H. Sahli, M.J.T. Reinders, J. Cornelis, “Eye Activity Detection and Recognition using Morphological Scale-space Decomposition,” ICPR Proceedings of the International Conference on Pattern Recognition (ICPR'00)-Volume 1 - Volume 1, 2000.

[19] R. Brunelli and T. Poggio, “Face Recognition: Features versus Templates,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 15, No. 10, Oct. 1993.

[20] Y. L. Tian, T. Kanade, and J. F. Cohn, “Dual-state Parametric Eye Tracking,” Proceedings of the Fourth IEEE International Conference on, 2000.

[21] T. Ohnishi, W. Chen, T. Kobayashi, Y. Honda, T. Saito, T. Nemoto, R. Kusunose, “Continuous Measurments of Eyeball Area and Spectrum Analysis Toward The Quntification of Rest Rhythm of Horse By Image Processing ,” ieee, pp 3363-3366, 2001.

[22] Hong Man, “Motion Track / Object Recognition,” Dec., 2002.

[23] I. Ravyseu, H. Sahliu, M.J.T. Reinderst, J. Cornelisn “Eye Activity Detection and Recognition using Morphological Scale-space Decomposition,” ieee cnf on Volume 1, Page(s):1080 - 1083, Sept. 2000.

[24] Z. Hammal, A. Caplier, “Eyes and Eyebrow Parametric Models for Automatic Segmentation,” ieee cnf Page(s):138 – 141, March 2004.

[25] Benno L. Petrig, Josef Bigun, and Marc G. Curchod, “Motion Estimation of Ocular Fundus Images,” ieee, Volume 3, 16-19 Page(s):691 – 694, Sept. 1996.

[26] Azzam F G Taktak, Sarah Simpson, Sam Patel and Georg Meyer, “Neural network analysis of oxygenation signals in infants during sleep,” IOP Publishing Ltd, PP N11-N22, 2000.

[27] T. Penzel, K. Kesper, V. Gross, H. F. Becker, C. Vogelmeier, “Problems in Automatic Sleep Scoring Applied to Sleep Apnea,” ieee cnf, Volume 1, 17-21 Page(s):358 – 361, Sept. 2003.

[28] D. Barschdorff, D. Gerherdt, “Rapid Eye Movement Detection in Infants Using A Neural Network,” ieee cnf, Volume 3, 31 Page(s):935 – 936, Nov. 1996.

[29] Theodore A. Camus, Richard Wildes, “Reliable and Fast Eye Finding in Close-up Images,” ieee cnf, Volume 1, 11-15 Page(s):389 – 394, Aug. 2002.

[30] Fernando De La Torre, Carlos Javier Garcia Rubio, Elisa Martinez, “Subspace Eyetracking for Driver Warning,” ieee cnf, Volume 3, 14-17 Page(s):III - 329-32, Sept. 2003.

[31] Samuel A. Mickelson, “The use of sleep tests for suspected sleep disordered,” Ear, Nose & Throat Journal, Sept, 1999.

[32] T.A. Camus, R. Wildes, O. Toronto, “Reliable and Fast Eye Finding in Close-up Images,” 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1, p. 10389, 2002.

全文公開日期本全文未授權公開 (校內網路)
全文公開日期本全文未授權公開 (校外網路)
全文公開日期本全文未授權公開 (國家圖書館：臺灣博碩士論文系統)

簡易檢索 / 詳目顯示

相關論文