臺灣博碩士論文加值系統

長久以來，人類手掌紋路之分析一直為許多不同領域專家所興趣之議題。包括生物特徵認證與辨識研究，心理學，以及命理學等領域。本文將實作一個自動掌紋命理分析系統，利用影像處理等相關技術，其中包括去除背景與影像二值化的閥值選取，經實驗，從長條圖中找出其相關特性，以期達到自動化選取閥值的目的；接著利用幾種常見的細化演算法，針對我們的掌紋影像作處理，比較處理結果並選取出較佳的演算法；還有針對掌紋中的三大主線，也就是命理學上所稱之感情線、智慧線與生命線，進行主線特徵抽取，抽取的過程中我們運用了線段追蹤法與線段補點法，將主線的趨勢記錄下來，之後還針對部分主線尾端，做後處理的修正，以期能得到更精確的尾端部分；然後利用抽取出來的主線，計算其週邊的支線數量。最後，利用上述處理步驟中所得到之掌紋資訊，送入我們的掌紋命理分析系統，進行命理分析。在本論文中，對於系統之各項處理步驟，以及實驗的過程與細節，將會在文中做進一步的探討。

Throughout the course of its development, palmprint features have always been studied and analyzed by experts from diverse fields, such as biometric identification, psychology, and fate theory. So far, papers about palmprints are mostly concerned about applications like personal identification; analyses about its application in palmistry are fewer. Therefore, in this paper, we put into practice a system that analyzes palmistry. With the help of image processing techniques, we transmit the information extracted from the palmprint image into our analyzing system, and have the palmprint assayed. Further discussion about each processing step of the system and details about the experiments are also included in the paper.

第一章 緒論 1
1.1 研究動機與目的 1
1.2 論文組織架構簡介 2
第二章 掌紋特徵介紹 4
2.1 掌紋身分識別 4
2.2 手相命理學介紹 6
2.2.1掌丘介紹 7
2.2.2掌紋介紹 8
第三章 掌紋影像處理 10
3.1 掌紋影像擷取 10
3.2 去背景閥值分析 13
3.2.1 Otsu Method 17
3.2.2 Mode Method 21
3.2.3線性迴歸（Linear Regression） 23
3.3 主體物件二值化閥值分析 28
3.4 條件式擴張 32
3.5 平滑處理 34
3.6 左右兩邊界方程式 35
3.7 細化演算法探討 37
3.7.1中軸法 38
3.7.2剝皮法 40
3.7.3細化演算法特性比較 46
3.8 主線特徵抽取 49
3.8.1區塊移動 49
3.8.2主線追蹤 50
3.8.3主線補點 53
3.9 感情線尾端修正與主軸定位 55
3.9.1感情線尾端修正 55
3.9.2主軸定位與曲率判斷 58
3.10 支線數量估計 60
第四章 掌紋命理分析 64
4.1 命理分析規則 64
4.1.1感情線 64
4.1.2智慧線 66
4.1.3生命線 67
4.2 掌紋命理分析 69
第五章 實驗結果 72
第六章 結論及未來研究方向. 79
參考文獻 81
附錄：英文論文

[1] W. W. Boles and S. Y. T. Chu, “Personal Identification Using Images of Human Palm”, TENCON’97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications, Proceendings of IEEE, Vol. 1, 1997.
[2] 李鐵筆著, “手掌乾坤”, 益群書局, 2001.
[3] N. Otsu, “A Thresholding Selection Method from Gray-scale Histogram”, IEEE Transactions on System Man Cybernet, Vol. SMC-8, pp. 62-66, 1978.
[4] J. M. S. Prewitt and M. L. Mendelsohn, “The Analysis of Cell Images”, in Ann. New York Acad. Sci. Vol. 128, pp. 1035-1053, 1966.
[5] S. Rodtook and Y. Rangsanseri, “Adaptive Thresholding of Document Images Based on Laplacian Sign”, IEEE, pp. 501-505, 2001.
[6] R. Jain, R. Kasturi and B. G. Schunck (ed.), “Machine Vision”, McGraw-Hill, 1995.
[7] G. Peters (ed.), “Fuzzy Linear Regression with Fuzzy Intervals”, Fuzzy Sets and Systems, 1994.
[8] J. R. Parker (ed.), “Algorithms For Image Processing and Computer Vision”, John Wiley & Sonics Inc. Pub. , 1996.
[9] R. Gonzalez and R. Woods (ed.), “Digital Image Processing”, Addsion-Wesley Publishing Company, 1992.
[10] F. W. Stentiford and R. G. Mortimer, “Some New Heuristics for Thinning Bonary Handprinted Characters for OCR”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 13, No. 1, pp. 81-84, 1983.
[11] S. Zhang and K. S. Suen, “A thinning Algorithm for Discrete Binary Images”. Processing of the International Conference on Computers and Application. Beijing, China, pp. 879-886, 1984.
[12] C. M. Holt, A. M. Stewart Clint, and R. H. Perrott, “An Improved Parallel Thinning Algorithm”, Communications of the ACM, Vol. 30, No. 2, pp. 156-160, 1987.
[13] J. Drummond, R. V. Essen, and P. Boulerie, “Some Considerations on Vectorization Algorithms”, ITC Journal, No. 3, pp. 153-157, 1991.
[14] 許紹龍著, “手相與面相”, 隆泉書局, 1986.