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研究生: 張嘉晏
Chia-Yen Chang
論文名稱: 以Fuzzy C-Means硬體架構為基礎之快速影像分割之研究
Fuzzy C-Means Architecture for Fast Image Segmentation
指導教授: 黃文吉
Hwang, Wen-Jyi
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2010
畢業學年度: 98
語文別: 中文
論文頁數: 58
中文關鍵詞: 可程式邏輯陣列FCM演算法影像分割可程式化系統晶片
論文種類: 學術論文
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  • 本論文根據文獻[6]將FCM演算法m值設定為2之硬體架構延伸於適用所有m值的FCM演算法硬體架構。此架構以管線化實現,並且具有平行計算的能力。在論文中我們使用查表法(lookup table)與泰勒展開式,推導出開根號計算之硬體電路,以減少根號運算時所耗費的硬體資源。此外,本論文將模糊分群演算法應用於影像分割的處理,並將FCM演算法之硬體架構延伸至FCM with spatial constraint演算法上,以改善雜訊影響影像分割的結果,以及保有硬體架構平行計算之優點。由實驗結果顯示,所提出的硬體架構能夠快速並有效的將模糊分群演算法應用在影像分割的處理。

    中文摘要 i 致謝 ii 附圖目錄 v 附表目錄 viii 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 3 1.3 全文架構 5 第二章 理論基礎與技術背景 6 2.1 Fuzzy C-Means 演算法 6 2.2 Fuzzy C-Means with spatial constraint 演算法 9 2.3 SOPC系統整合設計 11 第三章 基礎電路架構介紹 14 3.1 FCM 14 3.1.1 Pre-computation unit 15 3.1.2 Membership coefficients updating unit 24 3.1.3 Centroid updating unit 29 3.1.4 Cost function computation unit 31 3.2 FCM with spatial constraint 33 3.2.1 Mean computation unit 33 3.2.2 Fuzzy clustering unit 35 第四章 實驗結果與數據探討 39 4.1 開發平台與實驗環境介紹 39 4.2 實驗數據的呈現與討論 41 第五章 結論 56 參考著作 57

    [1]Bezdek, J. C., "Fuzzy mathematics in pattern classification," 1973.

    [2] S.Chen, D.Zhang, "Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure," IEEE Systems, Man, and Cybernetics, 2004.

    [3]J. Garcia-Lamont, L.M. Flores-Nava, F. Gomez-Castaneda, J.A. Moreno-Cadenas, "CMOS Analog Circuit for Fuzzy C-Means Clustering," IEEE Proc. 5th BiannualWorld Automation Congress, 2002.

    [4] P. Hung, H. Fahmy, O. Mencer, and M. J. Flynn, "Fast Division Algorithm with a Small Lookup Table," IEEE Asilomar Conference on Signals, Systems, and Computers, pp.1465-1468, 1999.

    [5] J.F. Kolen and T. Hutcheson, "Reducing the Time Complexity of the Fuzzy C-Means Algorithm," IEEE Trans. Fuzzy Systems, pp. 263-267, Vol. 10, 2002.

    [6] Hui-Ya Li, Cheng-Tsun Yang, Wen-Jyi Hwang, “Efficient VLSI Architecture for Fuzzy C-Means Clustering in Reconfigurable Hardware”, Proc. IEEE International conference on Frontier of Computer Science and Technology, 2009, p.168-174.

    [7] J. D. Lee, Z. X. Hunag," Automatic color image segmentation with Fuzzy c-means algorithm, " 1990.

    [8] J. Lazaro, J. Arias, J. L. Martin, C. Cuadrado and A. Astarloa, "Implementation
    of a Modified Fuzzy C-Means Clustering Algorithm for Realtime Applications," Microprocessors and Microsystems, pp. 375-380, 2005.

    [9] NIOS II Processor Reference Handbook, 2007, Altera Corporation. http://www. altera.com/literature/lit-nio2.jsp

    [10] Pal, N. R. and Bezdek, J. C., 1995. "On cluster validity for the fuzzy c-means model. " IEEE Transactions on Fuzzy System, Vol.3, No.3, p.370-379.

    [11] Pei Jihong, Yang Xuan, Gao Xinbo, and Xie Weixing, "Weighting exponent m in fuzzy C-means (FCM) clustering algorithm. " Proc. SPIE Vol. 4554, p. 246-251.

    [12] Stratix II Device Handbook, 2008, Altera Corporation.http:// www.altera.com/ literature/ lit-nio2.jsp.

    [13] Udupa, J.K., Samarasekera, S.: Fuzzy connectedness and object definition: theory,algorithm and applications in image segmentation. Graph. Models Image Process. 8(3),246–261 (1996)

    [14] Vriend. S.P. van Gaans, P.F.M., Middelburg, J. and de Nijs. A. 1988. "The application of fuzzy c-means cluster analysis and nonlinear mapping to geochemical datasets: examples from Portugal. " Appl. Geochem., 3: 2 13-224.

    [15] Yamany, S.M., Farag, A.A., Hsu, S.: A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems. Pattern Recognit. Lett. 20, 1431–1438 (1999)

    [16] Zimmermann, Hans J., 1990. "Fuzzy set theory and its applications. " Kluwer Academic Publishers, Boston.

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