簡易檢索 / 詳目顯示

研究生: 栗宇平
Yu-ping Lee
論文名稱: 混合空間域與頻域的影像套合
A combination of feature based and fourier based image registration
指導教授: 許新添
Hsin-Teng Hsu
口試委員: 黃騰毅
Teng-Yi Huang
陳志明
Chih-Ming Chen
學位類別: 碩士
Master
系所名稱: 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年: 2005
畢業學年度: 93
語文別: 中文
論文頁數: 93
中文關鍵詞: 廣義霍夫轉換Harris corner detector極座標傅利葉轉換影像套合
外文關鍵詞: Harris corner detector, image registration, polar Fourier transform, generalized Hough transform
相關次數: 點閱:418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

對於不同時間、不同影像感測器、不同觀點所拍攝的影像于以疊合,為重要的影像套合課題。幾何上,影像套合校準了參考與待測兩影像。多年來,考慮不同變數與不同問題上,發展出各類型的套合方法。當一張待測影像相較於參考影像發生位移、旋轉與縮放時,其傅利葉頻域上會有相對應的關係。傅利葉轉換的位移定理是一個基本的概念,利用phase correlation可以強健的估算出位移參數。接著,使用對數極座標傅利葉轉換後,空間中旋轉與縮放關係被簡化成頻譜中的位移關係,使其亦可以phase correlation方法解出。由於涉及傅利葉轉換的部份皆以FFT演算法來求取,故整個方法被稱為FFT-based影像套合方法。
FFT-based影像套合方法的其中一種限制為,當參考影像與待測影像的取景大小差異過大時,無法將兩影像于以套合。在本篇論文中,我們提出了一種基於特徵點的方法,來粗略的標定出待測影像中最相近於參考影像的區域。首先,以Harris corner detector偵測出特徵點為方法的第一步。再來,我們調整了廣義霍夫轉換,使其能根據兩影像的特徵點,找出待測影像相似於參考影像的區域。最後,我們提出了結合以上基於特徵點與傅利葉頻域的影像套合方法。於實驗中,我們證明了準確的套合結果是可達成的。


Image registration is a fundamental task that matches the images taken at different times, from different sensors, or from different viewpoints. The registration geometrically aligns two images - the reference and sensed images. Over the years, a variety of techniques has been developed for various types of data and problem. When a sensed image is translated, rotated, and scaled with respect to the reference image, it has a counterpart in the Fourier domain. The basic notion is the shift property of the Fourier transform, which allows robust estimation of translations using phase correlation. Using log-polar Fourier transform, rotation and scaling are reduced to translation, which are also estimated by phase correlation. The method is called FFT-based image registration because of the utilization of the FFT algorithm to compute Fourier transform.
One constraint of FFT-based image registration is that it can not be registered if the size of view between reference and sensed images differs significantly. In this thesis, we propose a feature-based method to coarsely locate the region of the sensed image which is supposedly the most similar to the reference image. The first step of the algorithm detects characteristic points by Harris corner detector followed by modifying the GHT method to extract similitude based on the detected feature points. At the end, the combination of feature based and Fourier based image registration can be achieved. Our experiments show that the accuracy of the resulting registration can be reached.

第一章 緒 論 1 1.1 研究背景與簡介 1 1.2 影像套合方法介紹 2 1.3 基於快速傅立葉轉換之影像套合方法 3 1.4 研究目的與方法 3 1.5 論文架構 5 第二章 基於頻域之影像套合技術 6 2.1 傅立葉轉換平移、旋轉與縮放性質 7 2.2 極座標傅立葉轉換 10 2.3 類極座標(Pseudo-polar)傅立葉轉換 14 2.4 Chirp-Z Transform 17 2.5 類極座標傅立葉轉換到極座標傅立葉轉換 20 第三章 以Harris特徵點進行搜尋 25 3.1 Harris特徵點擷取演算法 25 3.2 廣義霍夫轉換 31 3.3 以Harris特徵點之類GHT法進行搜尋 39 第四章 實驗結果 43 4.1 實驗ㄧ-極座標傅立葉轉換頻譜分析 43 4.2 實驗二-FFT based image registration 52 4.3 實驗三-混合空間域與頻域之影像套合 62 4.4 討論 73 第五章 結論 78 5.1 結果與討論 78 5.2 未來研究方向 79 參考文獻 80

[1] Lisa Gottesfeld Brown, "A survey of image registration techniques," ACM Computing Surveys, vol. 24, no. 4, pp. 325-376, Dec. 1992.
[2] Barbara Zitova, Jan Flusser, " Image registration methods: a survey, " Image and Vision Computing, vol. 21, no. 11, pp. 977-1000, Oct. 2003.
[3] J. Canny, "A computational approach to edge detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, pp. 679–698,1986.
[4] D. Marr, E. Hildreth, "Theory of edge detection", Proceedings of the Royal Society of London, B 207 (1980) 187–217.
[5] C. Harris, M. Stephens, "A combined corner and edge detector," Proceedings of The Fourth Alvey Vision Conference, Manchester, pp. 147-151, 1988.
[6] A. Goshtasby, G.C. Stockman, C.V. Page, "A region-based approach to digital image registration with subpixel accuracy", IEEE Transactions on Geoscience and Remote Sensing, vol. 24, no. 3, pp. 390-399,1986.
[7] A. Goshtasby, G.C. Stockman, "Point pattern matching using convex hull edges", IEEE Transactions on Systems, Man, and Cybernetics, vol. 15, no. 5, pp. 631-637, 1985.
[8] J. Flusser, "Object matching by means of matching likelihood coefficients", Pattern Recognition Letters, vol. 16, pp. 893–900, 1995.
[9] C.Y. Wang, H. Sun, S. Yadas, A. Rosenfeld, "Some experiments in relaxation image matching using corner features", Pattern Recognition, vol. 16, no. 2, pp. 167–182, 1983.
[10] R.J. Althof, M.G.J. Wind, J.T. Dobbins, "A rapid and automatic image registration algorithm with subpixel accuracy", IEEE Transactions on Medical Imaging, vol. 16, pp. 308–316,1997.
[11] H. Hanaizumi, S. Fujimura, "An automated method for registration of satellite remote sensing images", Proceedings of the International Geoscience and Remote Sensing Symposium IGARSS’93, Tokyo, Japan, pp. 1348–1350, 1993.
[12] P. Viola, W.M. Wells, "Alignment by maximization of mutual information", International Journal of Computer Vision , vol. 24, no. 2, pp. 137–154, 1997.
[13] C.D. Kuglin and D. C. Hines, "The phase correlation image alignment method," Proc. IEEE Conf. Cybernetics and Society, pp. 163-165, Sep. 1975.
[14] S. Reddy and B.N. Chatterji, "An FFT-based technique for translation, rotation, and scale-invariant image registration," IEEE IEEE Transactions on Image Processing, vol. 3, no. 8, pp. 1266–1270, Aug. 1996.
[15] Yosi Keller, Amir Averbuch, and Moshe Israeli, "Pseudopolar-based estimation of large translations, rotations, and scalings in images," IEEE Transactions on Image Processing, vol. 14, no. 1, Jan. 2005.
[16] Mankun Xu, Xijian Ping, "An improved fast fourier transform in polar coordinate system," Proceedings of SPIE - The International Society for Optical Engineering, vol. 5286, no. 1, pp. 445-448, 2003.
[17] A. Averbuch, R.R. Coifman, D.L. Donoho, M. Elad, M. Israeli, "Accurate and fast discrete polar fourier transform," Record of the Asilomar Conference on Signals, Systems and Computers, vol 2, Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, pp. 1933-1937, 2003.
[18] P.V.C. Hough, "Method and means for recognizing complex patterns," U. S. Patent 3,069,654, Dec. 18,1962.
[19] D.H. Ballard, "Generalizing the Hough transform to detect arbitrary shapes," Pattern Recognition, vol. 13, no. 2, pp. 111-122, 1981.
[20] Alan V. Oppenheim, Ronald W. Schafer, John R. Buck, Discrete-Time Signal Processing, second ed., Prentice Hall, Inc., 1999.
[21] H.P. Moravec, "Toward automatic visual obstacle avoidance," Proc. of 5th International Joint Conference on Artifitial Intelligence, pp.584, Aug. 1977.
[22] P.K. Ser and W.C Siu, "A new generalized Hough transform for the detection of irregular objects," Journal of Visual Communication and Image Representation 6, no. 3, pp. 256-264, Sept. 1995.
[23] Langi, Z.R., Soemintapura,K., Mengko, T.L., and Kinsner,W., "Multifractal Measures of Image Quality," IEEE ICICS´97, 2D1.5, pp.726-730,1997.
[24] Hassan Foroosh, Josiane B. Zerubia, Marc Berthod, "Extension of Phase Correlation to Subpixel Registration",IEEE Transactions on Image Processing, vol. 11, no. 3, pp. 188-199, March 2002.
[25] 張智星, MATLAB程式設計與應用, 清蔚科技, 2000.

無法下載圖示
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)
QR CODE