跳到主要內容

簡易檢索 / 詳目顯示

回結果列表
研究生： 丘前恕
Chien-Shu Chiu
論文名稱： 顯微照像的快速自動對焦技術
A Fast Autofocus Technique for Microscope Imaging
指導教授： 曾定章
Din-Chang Tseng
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
畢業學年度： 96
語文別： 中文
論文頁數： 116
中文關鍵詞： 自動對焦 、影像處理 、自動光學檢測
外文關鍵詞： autofocus, image processing, automatic optical inspection
相關次數： 點閱：15  下載：0 
分享至:
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

    • 在本論文中，我們發展出顯微照像的快速自動對焦技術，主要以解決目前在TFT-LCD及彩色濾光片 (color filter, CF) 面板產業所使用的自動光學檢測機 (automatic optical inspection, AOI) 及雷射修補機 (laser repair, LR) 所需的自動對焦問題為基礎，提供ㄧ個快速、穩定、且可靠的顯微照像快速自動對焦技術。
    我們依據光學成像原理以及影像處理的概念，使用快速傅立葉轉換 (Fast Fourier Transform, FFT)、離散餘弦轉換 (Discrete Cosine Transform, DCT)、小波轉換 (Discrete Wavelet Transform, DWT)、及理想高通濾波器 (Ideal high pass filter, IHPF)、巴特沃斯高通濾波器 (Butterworth high pass filter, BHPF)、高斯高通濾波器 (Gaussian high pass filter, GHPF)、理想低通濾波器 (Ideal low pass filter, ILPF)、巴特沃斯低通濾波器 (Butterworth low pass filter, BLPF)、高斯低通濾波器 (Gaussian low pass filter, GLPF)等頻率域的濾波器、及空間域的濾波器ㄧ次差分平方和 (energy of gradient magnitude, EOGM) 與二次差分平方和 (energy of image Laplacian, EOIL) 等技術來分析顯微影像的清晰程度。並將上述技術結合二次曲線近似、三次曲線近似、與高斯曲線近似等對焦搜尋演算法 (autofocus searching algorithm)，探討其移動間距、取樣點數、對焦速度、及對焦穩定性與可靠度。為了達到快速、穩定與可靠等特性，我們提出二種判定對焦量測準則優劣的準則：(i) 雜訊的抵抗能力；(ii) 計算複雜度；並提出二種判定對焦搜尋演算法優劣的準則：(i) 移動取像次數；(ii) 正確對焦的穩定性與可靠度。我們認為對焦量測準則與對焦搜尋演算法有顯著的相關性，因此我們依據對焦量測準則的雜訊抵抗能力、計算複雜度、及對焦搜尋演算法所需的移動取像次數來比較對焦量測準則；最後以大量的自動對焦實驗來證實，我們發展出的顯微照像快速自動對焦技術可以達到快速、穩定與可靠的結果。

    In this thesis, we develop a fast autofocus technique for microscope imaging, In order to solve the problem of focusing automatically in automatic optics inspection machine (AOI) and laser repair machine (LR) that used in TFT-LCD and color filter panel industry, we will offer a fast, steady, and reliable autofocus technique.
    We follow the principle of the geometric optics model of image formation and the concept of the image processing, use Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), frequency domain filter (Ideal high pass filter, IHPF),(Butterworth high pass filter, BHPF),(Gaussian high pass filter, GHPF),(Ideal low pass filter, ILPF),(Butterworth low pass filter, BLPF),(Gaussian low pass filter, GLPF), and spacial domain filter (energy of gradient magnitude, EOGM),(energy of image Laplacian, EOIL) technologies to analyze the focusing degree of the image of microscopes. Also, we combine above-mentioned technologies to the quadratic curve fitting approximate, the cubic curve fitting approximate and gaussian curve fitting approximate etc, autofocus searching algorithm. Probe into the moving step, sampling times, focus speed, and focus stability and reliability.
    In order to reach characteristics such as fast, steady and reliable, we propose two kinds of good and bad criteria of judging focusing criterion: (i) Ability of resisting of the noise; (ii) Calculating complexity; Propose two kinds of good and bad criteria of judging autofocus searching algorithm: (i) Move and sampling times; (ii) Stability and reliability of the correct focusing. We think the focus criterion has apparent dependence to autofocus searching algorithm, therefore we compare the focus criterion base on the ability of resisting of the noise, calculating complexity, and the sampling times of autofocus searching algorithm. Finally, we use a large number of focus experiment to prove we develop a fast autofocus technique for microscope imaging, which can reach fast, steady and reliable results.

    摘 要 .............................................. i 誌 謝 .............................................. iv 目 錄 .............................................. v 圖目錄 .............................................. vii 表目錄 .............................................. x 第一章 緒論 ......................................... 1 1.1 研究動機 ..................................... 1 1.2 硬體架構 ..................................... 2 1.2.1 顯微鏡規格 ............................. 3 1.2.2 CCD攝影機與取像卡規格 .................. 3 1.2.3 Z軸馬達、運動控制卡與光學尺定位移動平台. 5 1.3 軟體架構 ..................................... 6 1.4 論文架構 ..................................... 9 第二章 相關研究 ..................................... 10 2.1 對焦量測準則 ................................. 10 2.1.1頻率域為主的演算法 ...................... 11 2.1.2空間域為主的演算法 ...................... 12 2.1.3統計學為主的演算法 ...................... 13 2.2 對焦搜尋演算法 ............................... 13 2.2.1 全域搜法 ............................... 13 2.2.2 二分搜尋法 ............................. 14 2.2.3 費柏納數列搜尋法 ....................... 14 2.2.4 百分比下降搜尋法 ....................... 15 2.2.5 區域中心搜尋法 ......................... 15 第三章 光學成像原理與影像頻譜分析 ................... 17 3.1 光學成像原理 ................................. 17 3.1.1 點擴張函式與光學轉換函式 ............... 18 3.1.2 高斯點擴張函式模型 ..................... 20 3.1.3 邊波瓣效應 ............................. 21 3.2 對焦影像頻譜分析 ............................. 22 第四章 對焦量測準則分析與優劣評估 ................... 25 4.1 快速傅立葉轉換與離散餘弦轉換轉換頻譜分析 ..... 25 4.2 頻率域高通濾波器分析 ......................... 34 4.3 頻率域低通濾波器分析 ......................... 43 4.4 小波轉換 ..................................... 51 4.5 空間域Gradient與Laplacian濾波 ................ 66 4.6 對焦量測準則的優劣評估 ....................... 72 第五章 對焦搜尋演算法分析與優劣評估 ................. 79 5.1 對焦量測準則與對焦搜尋演算法的相關性分析 ..... 79 5.2 曲線近似演算法 ............................... 80 5.2.1 二次曲線與三次曲線近似演算法 ........... 80 5.2.2 高斯曲線近似演算法 ..................... 84 5.3 對焦搜尋演算法取像間距的定義 ................. 87 第六章 實驗與討論 ................................... 90 6.1 對焦速度分析 ................................. 90 6.2 對焦穩定性與可靠度分析 ....................... 92 6.2.1 系統穩定性實驗分析 ..................... 92 6.2.2 系統可靠度實驗分析 ..................... 95 第七章 結論與未來工作 ...............................100 參考文獻 ..............................................102

    [1] Baina, J. and J. Dublet,“Automatic focus and iris control for video cameras,”in Proc. of the Fifth Int. Conf. on Image Processing and its Applications, Edinburgh, Jul.4-6, 1995, pp.232-235.
    [2] Boddeke, F., V. L. Van, H. Netten, and L. Toung, “Autofocusing in microscopy based on the OTF and sampling,” Bioimaging, vol.2, pp.193-203, 1994.
    [3] Chern, N. K., P. A. Neow, and M. H. Ang Jr., “Practical issues in pixel-based autofocusing for machine vision,”in Proc. of the IEEE Int. Conf. on Robotics and Automation, Seoul, Korea, May 21-26, 2001, pp2791-2796.
    [4] Gonzalez, R. C. and R. E. Woods, Digital Image Processing, 2nd edition, Prentice-Hall, Upper Saddle River, NJ, 2002.
    [5] He, J., R. Zhou and Z. Hong,“Modified fast climbing serach auto-focus algorithm with adaptive step size searching technique for digital camera,” IEEE Trans. on Consumer Electronics, vol.49, pp257-262, May 2003.
    [6] Hecht, E., Optics, Addison-Wesley Publishing Company, 1987.
    [7] Kesseler, W. and F. Jan,“Analytical model of autofocus system with CCD camera,”in Proc. Int. Conf. on Sensor Camera System for Scientific, Industrial and Digital Photography Application, vol.3965, pp396-380, May 2000.
    [8] Kim, Y., June-Sok Lee, A. W. Morales, and Sung-Jea Ko, “A video camera system with enhanced zoom tracking and auto white balance,”IEEE Trans. on Consumer Electronics, vol.48, no.3, pp428-434, Aug. 2002.
    [9] Krotkov, E. P.,“Focusing,”International Journal of Computer Vision, vol.1, pp.223-237, 1987.
    [10] Li, F. and H. Lin,“A fast auto focusing method for digital still camera,”in Proc. Int. Conf. on Mechine Learning and Cybernetics, Aug.18-21, 2005, vol.8, pp5001- 5005.
    [11] Ligthart, G. and F. Groen,“A comparison of different autofocus algorithms”, in Proc. Int. Conf. on Pattern Recognition, Munich, Oct. 19-22, 1982, pp.597-600.
    [12] Mallat, S., ”A compact multiresolution representation: The wavelet model.”in Proc. IEEE Int. Conf. on Computer Vision, Washington DC, 1987, pp.2-7.
    [13] Nayar, S. K. and Y. Nakagawa,“Shape from focus,” in Proc. IEEE Int. Conf. on Robotics and automation, May 13-18, 1990, vol.2, pp.218-225.
    [14] Schlag, J. F., A. C. Sanderson, C. P. Neuman, and F. C. Wimberly, Implementation of automatic focusing algorithms for a computer vision system with camera control, CMU-RI-TR-83-14, Robotics Institute, Carnegie-mellon University, 1983.
    [15] Shen, Chun-Hung and H. H. Chen,“Robust focus measure for low-contrast images,”in Proc. Int. Conf. on Consumer Electronics, Jan. 7-11, 2006, pp.69-70.
    [16] Song, Yu, and L. Mantian,”Research on SUSAN based auto-focusing algorithm for optical microscope application,”in Proc. IEEE Int. Conf. on Mechatronics and Automation, Luoyang, China, June 25-28, 2006, pp1237-1241.
    [17] Subbarao, M. and J. K. Tyan,“Selecting the optimal focus measure for autofocusing and depth-from-focus,”in Proc. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no.8, pp864-870, Aug. 1998.
    [18] Subbarao, M., T.S. Choi, and A. Nikzad,“Focusing techniques”, Journal of Optical Engineering, vol.32, no.11, pp.2824-2836, Nov. 1993.
    [19] Subbarao, M.,“Parallel depth recovery by changing camera parameters,”in Proc. The 2nd Int. Conf. on Computer Vision, Florida, Dec.5-8, 1986, pp.149-155.
    [20] Tenenbaum, J. M., Accommodation in computer vision, Ph.D. Dissertation, Stanford University, Stanford, CA, 1970.
    [21] Tseng, Din-Chang, Image Processing, Lecture Note, Institute of Computer Science and Information Engineering, National Central Univ., 2007.
    [22] Widjaja, J. and S. Jutamulia,“Wavelet transform-based autofocus camera systems,”in Proc. of the IEEE Asia-Pacific Conf. on Circuits and Systems, Chiangmai, Nov.24-27, 1998, pp.49-51.
    [23] Xiong,Y. and S. A. Shafer,“ Depth from focusing and defocusing,”in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, New York, June 15-17, 1993, pp.68-73.
    [24] Yang, G. and B.J Nelson,“Wavelet-based autofocusing and unsupervised segmentation of microscopic images,”in Proc. Int. Conf. on Intelligent Robots and Systems, Minneapolis, MN, Oct.27-31, 2003, vol.3, pp.2143-2148.
    [25] Yeo T., S. O. Jayasooriah, and R. Sinniah, “Autofocusing for tissues microscopy,”Image and Vision Computing, vol.11, no.10, pp.629-639, 1993.

    QR CODE
    :::