跳到主要內容

臺灣博碩士論文加值系統

(35.175.191.36) 您好!臺灣時間:2021/08/01 00:12
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:郭光哲
研究生(外文):Kuang-Che Kuo
論文名稱:以模糊邏輯為基礎的智慧型輪椅自動充電與臉部控制法之研究
論文名稱(外文):A Study of Fuzzy-based Automatic Charge and Face Control for Intelligent Wheelchairs
指導教授:蔡舜宏蔡舜宏引用關係
口試委員:陶金旺羅吉昌練光祐
口試日期:2012-07-20
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:自動化科技研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:72
中文關鍵詞:視覺系統模糊控制器智慧型輪椅人臉偵測人臉辨識顏色辨識
外文關鍵詞:Vision SystemColor DetectionFuzzy ControllerIntelligent WheelchairFace DetectionFace Recognition
相關次數:
  • 被引用被引用:3
  • 點閱點閱:127
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本論文主要開發一具人機介面、硬體架構及智慧型控制器的智慧型輪椅。本輪椅提供使用者一個即時的人臉辨識及人臉控制輪椅之系統,並提出一個以模糊和影像為基底的自動充電方法。首先,基於電動輪椅本身硬體架構上,利用FPGA對輪椅控制器以控制輪椅的電壓以達到輪椅控制的目的,並架設各種感測器,藉以實現能夠進行多方面控制之系統。
在使用者辨識方面,利用特徵點尺度不變的特點作為人臉辨識的依據,以臉部特徵點進行臉部辨識,達到辨識輪椅使用者身分的目的。在人臉控制方面,本文使用色彩分析的方法,因此,統計樣本膚色的色彩範圍,並以此範圍內的膚色做為人臉偵測及定位的基準。為避免在不同光線下易造成誤判,本文以自適應閥值過濾眼睛特徵,並利用眼睛及嘴唇位置的幾何關係確定是否為正確的人臉特徵,最後定義出一條人臉位置基準線,判斷基準線的左右傾斜率以達到人臉控制的目的。
在輪椅的自動充電方面,我們提出了一個基於影像處理的新式自動充電方法,利用隨機圓偵測及橢圓偵測,我們可以從圓的重心得到充電位置及輪椅位置,經由這些資訊,輪椅可以自動校正角度及速度。除此之外,電子羅盤和超音波感測器被架設在輪椅上,以提供輪椅和充電位置的相關角度及距離。另外,我們採用模糊控制器控制輪椅,讓在調整輪椅角度及速度方面可以更精確,最後實驗的結果展現出我們提出的方法和機構是有效及可行的。


In this thesis, an intelligent wheelchair including the human-machine interface, the hardware architecture and the intelligent controller is implemented. The wheelchair provides the user a real-time face recognition and control system. Besides, a fuzzy based automatic charging method is proposed. Firstly, based on the primal hardware architecture, the FPGA technology is adopted to control the voltage of the wheelchair for reaching the control object and some sensors are installed for achieving the multiple control methods.
For user recognition, by utilizing the scale invariant character of the feature point for the face recognition basis and through the character point of the face, the face recognition is processed for achieving the object of user recognition. In the face control, the color analysis method is adopted. Therefore, the color ranges of statistical samples for skin color are gathered and the range is defined as the skin color range for the bases of the face detection and face localization. In order to avoid the misjudging under the different brightness, the self-adaptive threshold is propounded to filter the eyes and lips characters. In addition, the geometric position relation is used to examine the correct face features. Finally a baseline of the face position is defined to check the tilt rate of the baseline for achieving the purpose of the face control.
For automatic charge of wheelchair, we propose a novel automatic charging method based on the image detection. By utilizing the random circle detection (RCD) and ellipse detection method, we can obtain the relative position of charging position and wheel robot from the center of a circle. From the information, the wheel robot can adjust the angle and speed automatically. Besides, the electrical compass and the ultrasonic sensor are installed in the wheel robot for obtaining the relative angle and distance of wheel robot and the charging position. Moreover, we adopt the fuzzy controller to control the wheelchair for adjusting the angle and speed of wheel robot more precisely. Finally, the experiment results show that the proposed approach and the mechanism are effective and feasibility.


目 錄
中文摘要 i
英文摘要 ii
誌 謝 iv
目 錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 前言 1
1.2 研究動機 2
1.3 研究目的 2
1.4 論文架構 3
第二章 智慧型輪椅硬體與系統架構 5
2.1 前言 5
2.2 FPGA簡介 5
2.2.1 FPGA工作原理 5
2.2.2 FPGA基本特點 6
2.3 超音波感測器 6
2.3.1 串列模式 7
2.3.2 工作指令 7
2.4 電子羅盤 8
2.5 AX-12伺服馬達 10
2.6 控制器 12
2.6.1 控制器腳位說明 13
2.6.2 輪椅通訊協定 14
2.6.3 軟體及控制介面 15
2.7 攝影機 16
第三章 基於尺度不變特徵點的人臉辨識 17
3.1 前言 17
3.2 尺度空間的生成 18
3.3 檢測尺度空間的極值 21
3.4 準確的關鍵點位置 22
3.4.1 過濾對比度低的點 22
3.4.2 消除邊緣響應 23
3.5 關鍵點的方向分配 25
3.6 特徵描述子的生成 26
3.7 特徵點的匹配 27
第四章 應用於輪椅的人臉控制方法 28
4.1 色彩空間 29
4.2 膚色分割 29
4.3 基於模糊理論的膚色偵測方法 31
4.3.1 模糊理論 32
4.3.2 膚色偵測模糊系統設計 36
4.4 形態學 40
4.5 眼睛及嘴唇區域的偵測 43
4.5.1 頭髮顏色過濾 44
4.5.2 自適性閥值的眼睛偵測 45
4.5.3 嘴唇與眼睛的相對位置判斷 48
4.5.4 人臉基準線的輪椅動作判斷 50
第五章 基於模糊的輪椅自動充電方法 53
5.1 控制架構 54
5.2 影像處理演算法 54
5.2.1 隨機圓偵測及橢圓偵測 55
5.2.2 模糊控制器輸入參數計算 58
5.2.3 模糊控制器的設計 62
5.3 模擬與實驗 65
第六章 結論 68
參考文獻 69


參考文獻

[1] 內政部戶政司,http://www.doh.gov.tw/cht2006/index_populace.aspx
[2] 內政部統計處,http://www.moi.gov.tw/stat/
[3] 中華百科,http://wikiyou.tw/fpga/
[4] Robotis,http://www.robotis.com/xe/
[5] 維基百科,http://zh.wikipedia.org/zh-tw/MFC
[6] David G. Lowe, “Object recognition from local scale-invariant features,” International Conference on Computer Vision, Corfu, Greece, 1999, pp. 1150-1157.
[7] David G. Lowe, “Distinctive Image Features from Scale-invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, 2004, pp. 91-110.
[8] K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, Washington, DC, USA, 2005, pp 1615—1630.
[9] 劉宏娟,SIFT算法總結,http://wenku.baidu.com/view/5c5a542d7375a417866f8f68.html?from=rec&pos=1&weight=74&lastweight=59&count=5.
[10] M. Brown and David G. Lowe, “Invariant features from interest point groups,” British Machine Vision Conference, Cardiff, Wales, 2002, pp. 656-665.
[11] H. Noda, M. Niimi and J. Korekuni, “Simple and Efficient Colorization in YCbCr Color Space,” 18th International Conference on Pattern Recognition, Hong Kong 2006, pp. 685-688.
[12] B. Ahirwal, M. Khadtare and R. Mehta, “FPGA based system for color space transformation RGB to YIQ and YCbCr,” International Conference on Intelligent and Advanced Systems, Kuala Lumpur, Malaysia, 2007, pp. 1345-1349.
[13] S. Peng and Y. Wen, “Research based on the HSV humanoid robot soccer image processing,” Second International Conference on Communication Systems, Networks and Applications, Hong Long, 2010, pp.52-55.
[14] M. H. Yang, D. J. Kriegman and N. Ahuja, “Detecting faces in images: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no 1, 2002, pp. 34-58.
[15] A. Albiol, L. Torres and E. J. Delp, “Optimum color spaces for skin detection,” Proc. ICIP, Thessaloniki, Greece, 2001, pp. 122–124
[16] J. Yang, Z. Fu, T. Tan and W. Hu, “Skin Color Detection Using Multiple Cues,” Proc. ICPR, Cambridge, UK, vol. 1, 2004, pp. 632–635.
[17] J. Brand, J. S. Mason, M. Roach and M. Pawlewski, “Enhancing Face Detection in Colour Images using a Skin Probability Map,” Proc. of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, China, 2001, pp. 344–347.
[18] V. Vezhnevets, V. Sazonov and A. Andreeva, “A Survey on Pixel-Based Skin Color Detection Techniques,” Proc. Graphicon, Moscow, Russia, 2003, pp. 85–92
[19] F. A. Pujol, R. Espi, H. M. Mora and J. L. Sanchez, “A Fuzzy Approach to Skin Color Detection,” MICAI, Mexico City, Mexico, 2008, pp. 532-542.
[20] 李宗岳(2005),自適性臉部特徵擷取的動態人臉偵測,國立台灣師範大學,機電科技研究所,碩士論文。
[21] L. A. Zadeh, “Fuzzy sets,” Information and control, vol. 8, 1965, pp.338-353.
[22] C. Su and R. M. Haralick, “Recursive erosion, dilation, opening, and closing transforms,” IEEE Transactions on Image Processing, vol. 4, 1995, pp. 335-345.
[23] J. Y. Gil and R. Kimmel, “Efficient dilation, erosion, opening, and closing algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, 2002, pp. 1606-1617.
[24] R. C. Gonzalez and R. E. Woods, “Digital Image Processing 3/e,” Addison-Wesley, 2008, pp. 555-568.
[25] F. Chang and C. J. Chen, “A Component-Labeling Algorithm Using Contour Tracing Technique,” Seventh International Conference, Document Analysis and Recognition, Edinburgh, UK, 2003, pp. 741-745.
[26] K. Sobottka and I. Pitas, “Extraction of Facial Regions and Features Using Color and Shape Information,” IEEE, Proceedings of ICPR, Vienna, Austria, vol. 3, 1996, pp. 421-425.
[27] T.-H. S. Li and S.-J. Chang, “Autonomous Fuzzy Parking Control of a Car-Like Mobile Robot,” IEEE Transactions on systems, man, and cybernetics, vol. 33, 2003, pp. 451-465
[28] T.-H. S. Li, S.-J. Chang and Y. X. Chen, “Implementation of human-like driving skills by autonomous fuzzy behavior control on an FPGA-based car-like mobile robot,” IEEE Trans on Industrial Electronics, vol. 50, no.5, 2003, pp. 867-880.
[29] A. Ohata and M. Mio, “Parking control based on nonlinear trajectory control for low speed vehicles,” Proc. IEEE Int. Conf. Industrial Electronics, Kobe, Japan, 1991, pp. 107-112
[30] W. A. Daxwanger and G. K. Schmidt, “Skill-based visual parking control using neural and fuzzy networks,” Proc. IEEE Int. Conf. System, Man, Cybernetics, Vancouver, BC, Canada, vol. 2, 1995, pp. 1659-1664
[31] D. Lyon, “Parallel parking with curvature and nonholonomic constraints,” Proc. Symp. Intelligent Vehicles, Detroit, MI, USA, 1992, pp. 341-346.
[32] H. An, T. Yoshino, D. Kashimoto, M. Okubo, Y. Sakai, and T.Hamamoto, “Improvement of convergence to goal for wheeled mobile robot using parking motion,” Proc. IEEE Int. Conf. Intelligent Robots Systems, Kyongju, Korea, 1999, pp. 1693-1698.
[33] C. Pegard and E. M. Mouaddib, “A mobile robot using a panoramic view,” Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN, USA, 1996, pp. 89-94.
[34] Y. Yagi, Y. Nishizawa, and M. Yachida, “Map-based navigation for a mobile robot with omnidirectional image sensor COPIS,” IEEE Trans. Robot Automat, vol. 11, 1995, pp. 634-648.
[35] N. Winters, J. Gaspar, G. Lacey and J Santos, “Omni-directional Vision for Robot Navigation,” IEEE Workshop on Omnidirectional Vision, Hilton Head Island, SC, USA, 2000, pp. 21-28.
[36] J. Gaspar, N. Winters and J. Santos, “Vision-Based Navigation and Environmental Representations with an Omnidirectional Camera,” IEEE Transactions on Robotics and Automation, vol. 16, 2000, no.6, pp. 890-898.
[37] T. C. Chen and K. L. Chung, “An efficient randomized algorithm for detecting circles,” Computer Vision and Image Understanding, Academic Press, 2001, pp. 172-191.
[38] B. Lamiroy, L. Fritz, and O. Gaucher,“Robust circle detection,” Proc. Int. Conf. on Document Analysis and Recognition, Curitiba, Parana, Brazil, vol. 1, 2007, pp. 526-530.
[39] K. L. Chung and Y. H. Huang, “Speed up the computation of randomized algorithms for detecting lines, circles, and ellipses using novel tuning and LUT-based voting platform,” Applied Mathematics and Computation, vol. 190, 2007, pp. 132-149.
[40] K. Chattopadhyay, A. Acharya, A. Banerjee, J. Basu and A. Konar, ”Fast and Efficient Circle Detection Schemes for Digital Image,” First International Conference on Emerging Trends in Engineering and Technology, Nagpur, Maharashtra, 2008, pp. 128-133.
[41] X. Cao and F. Deravi, “An efficient method for multiple-circle detection,” Third International Conference on, Computer Vision, Proceedings, Osaka, Japan, 1990, pp. 144-147.
[42] Y. Xie and Q. Ji, “A New Efficient ellipse detection method,” icpr, 16th International Conference on Pattern Recognition, Quebec City, QC, Canada, vol. 2, 2002, pp.20957,
[43] The MathWorks, Matlab & Simulink Student Version –R2007a
[44] L. X. Wang and J. M. Mendel, “Generating fuzzy rules by learning from examples,” IEEE Transactions on Systems, Man, and Cybernetics, vol.22, no.6, 1992, pp.1414-1427.


QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊