臺灣博碩士論文加值系統

二張影像間的關聯性比較及評估是許多影像處理應用中的關鍵技術。我們以一個列向量來表示中心位於目標點的子影像視窗，然後就可以由兩張子影像的列向量來計算其關係性。本文將以向量長度及角度兩種幾何觀點，探討六種常被使用的影像相似度評估函數。依據它們的幾何特性，對九種不同的狀況解釋及驗證其優缺點，用以提供使用者針對不同的應用環境，選擇適當的影像相似度評估函數，以公平應用在不同的影像量度上。此外也針對使用這六種影像相似度評估函數之間不等式關係作探討，以提供交互選取使用時門檻值設定的依據。

The comparison and evaluation of the correlation measure between two images is the key component of many image processing applications. We represent the subimage in the window centered at the target point as a column vector. Then the correlation measure between two subimages is computed based on the two column vectors associated with the two subimages. This thesis discusses the six frequently used image similarity measure functions from two viewpoints of vector distance and angle. According to their geometric properties, nine cases are analyzed and their pros and cons are described. Thus, users may choose the proper image similarity measure function to deal with different images under different application environments. Besides, the inequality relations between these six image similarity measure functions are discussed to provide the guideline for the threshold setting.

中文摘要………………………………………………………………………i
英文摘要……………………………………………………………………ii
誌　　謝……………………………………………………………………iii
目　　錄……………………………………………………………………iv
圖 目 錄………………………………………………………………………v
表 目 錄……………………………………………………………………vi
一、緒論………………………………………………………………………1
1.1 研究動機與目的………………………………………………………1
1.2 論文組織………………………………………………………………4
二、評估函數…………………………………………………………………5
2.1 幾何意義與分類………………………………………………………5
2.2 位移向量……………………………………………………………10
2.3 門檻值(Threshold value) ………………………………………11
三、不同使用狀況下的評估函數表現……………………………………14
3.1 狀況一：不同的光照條件…………………………………………15
3.2 狀況二：均勻影像區域……………………………………………19
3.3 狀況三：線性遞變影像區域………………………………………21
3.4 狀況四：相同的殘餘向量…………………………………………25
3.5 狀況五：NCC對影像亮度的喜惡……………………………………29
3.6 狀況六：ZNCC對影像變異量的喜惡………………………………31
3.7 狀況七：距離基底評估函數對不同的門檻值……………………33
3.8 狀況八：角度基底評估函數均勻區域的反應……………………36
3.9 狀況九：角度基底評估函數對線性變化區域反應………………38
四、非參數轉換……………………………………………………………40
4.1 Nonparametric Transform演算法…………………………………40
4.2 運算子………………………………………………………………42
4.3 混合式比對(Hybrid Matching) …………………………………45
五、結論與未來工作………………………………………………………51
5.1 結論…………………………………………………………………51
5.2 未來工作……………………………………………………………51
參考文獻……………………………………………………………………53

[1] G. X. Ritter and J. N. Wilson, Handbook of Computer Vision Algorithms in Image Algebra, CRC Press, 1996, pp.225-254
[2] F. Dufaux, and F. Moscheni, “Motion estimation techniques for digital TV: A review and a new contribution,” Proc. IEEE, vol. 83, no. 6, June 1995
[3] L.G. Brown, “A survey of image registration techniques,” ACM Computing Survey, vol. 24, no. 4, Dec. 1992, pp. 325-376
[4] M. Kutter and F.A.P. Petitcolas, “A fair benchmark for image watermarking systems,” in Proc. 11th Int. Symp. on Electronic Imaging, vol. 3657. San Jose, CA: IS&T and SPIE, Jan. 25-27, 1999
[5] F.A.P. Petitcolas, R.J. Anderson, and M.G. Kuhn, “Information Hiding─A Survey,” Proc. IEEE, vol. 87, no. 7, Jul. 1999
[6] A.M. Eskicioglu and P.S. Fisher, “Image quality measures and their performance,” IEEE Trans. on Comm., Dec. 1995, pp. 2959-2965
[7] A.M. Eskicioglu and P.S. Fisher, “A survey of quality measures for gray scale image compression”, in Proc. 1993 Space and Earch Science Data Compression Workshop (NASA Conference Publication 3191), Snowbird, Utah, Apr. 2, 1993, pp. 49-61
[8] U.R. Dhond and J.K. Aggarwal, “Structure from Stereo — A Review,” IEEE Trans. System, Man, and Cybernetics, Vol. 19, No. 6, 1989, pp. 1489-1510
[9] Y. Rui, T. S. Huang, and Shih-Fu Chang, “Image Retrieval: Current Techniques, Promising Directions, and Open Issues,” Journal of Visual Communication and Image Representation 10, pp. 39-62, 1999
[10] Y. Alp Aslandogan and Clement T. Yu, “Techniques and Systems for Image and Video Retrieval,” IEEE Trans. on Knowledge and Data Engineeing, Vol. 11, No. 1, Jan./Feb., 1999, pp. 56-63
[11] M. Stricker and M. Orengo, “Similarity of color images,” in Proc. SPIE Storage and Retrieval for Image and Video Databases, 1995
[12] J. Martin and J. L. Crowley, “Experimental Comparison of Correlation Techniques,” in Int. Conf. on Intelligent Autonomous Systems, Karlsruhe, Germany, 1995
[13] J. Banks, M. Bennamoun, and P. Corke, “Fast and Robust Stereo Matching Algorithms for Mining Automation,” Digital Signal Processing 9, 137-148 (1999)
[14] A. M. Eskicioglu, "Quality measurement for monochrome compressed images in the past 25 years," Proc. 2000 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing Processing, Vol. 4, pp. 1907-1910
[15] O. Egger, P. Fleury, T. Ebrahimi, and M. Kunt, "Hight-Performance Compression of Visual Information--A Tutorial Review--Part I: Still Pictures," Proceedings of the IEEE, Vol.87, No.6, June 1999, pp. 976-1011
[16] Tom M. Apostol, Mathematical Analysis, Addison-Wesley, 1975, p.67