研究生: |
歐浩聲 Ou, Hao-Sheng |
---|---|
論文名稱: |
以Kernel為基礎之模糊分群演算法硬體架構實現 FPGA Implementation for Kernel-Based Fuzzy C-Means algorithm with Spatial Constraint |
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: | 2012 |
畢業學年度: | 100 |
語文別: | 中文 |
論文頁數: | 45 |
中文關鍵詞: | 可程式邏輯陣列 、KFCM演算法 、FCM-SC演算法 、系統程式晶片設計 、KFCM-SC演算法 |
英文關鍵詞: | FPGA, KFCM algorithm, FCM-SC algorithm, SOPC, KFCM-SC algorithm. |
論文種類: | 學術論文 |
相關次數: | 點閱:242 下載:13 |
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本論文根據文獻[12]以及文獻[17],以此兩則文獻中提到的FCM-SC分群演算法的硬體架構和KFCM演算法的硬體架構為基礎,實作以非線性高斯核函式為核距離計算之KFCM[12] 再加上空間資訊[17] 後的分群演算法硬體電路,具有管線化以及可以同時計算所有分群之權重係數的能力。此架構改良了以往KFCM分群演算法對於有雜訊的資料做分群的問題,並且配合KFCM本身可以對非線性資料分群效果較好的能力,所以能夠廣泛地使用在許多的分群資料上,並且都有良好的辨識率。本論文使用FPGA實現我們提出的硬體架構,並使用人工雜訊圖片作為實驗測試資料。實驗結果顯示本架構對於有雜訊的非線性資料分群效果確實較KFCM佳,且架構簡單提供了日後高度的延伸性。
Based on the FCM-SC (Fuzzy C-Mean with spatial constraint) architecture in reference [12] and the KFCM (Kernel-Based Fuzzy C-Means) architecture in reference [17], KFCM-SC (Kernel-Based Fuzzy C-Means with spatial constraint) hardware architecture is proposed here with non-linear Gaussian kernel function and spatial constraint. Moreover, the KFCM-SC architecture also takes the advantage of the pipeline and it can compute all of the membership coefficients and centers concurrently. Compared to KFCM architecture, KFCM-SC architecture improves the segmentation ability for noisy data by computing the spatial information. With these advantages, it can deal with the non-linear data due to the kernel function, KFCM-SC architecture can be applied to wide of data and it can achieve better segmentation results. KFCM-SC architecture is implemented on FPGA and tested with noisy picture data. The segmentation result shows that KFCM-SC architecture definitely has a better ability with non-linear noisy data compared to KFCM. Because of the simple architecture of the KFCM-SC, it can be extended easily.
[1] J. C. Bezdek, Fuzzy Mathematics in Pattern Classification, Cornell University: Ithaca, NY, USA, 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, pp.1907-1919, 2004.
[3] W. Cai, S. Chen, D. Zhang, Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognition, Vol. 40, pp. 825-838, 2007.
[4] M. Filippone, F. Camastra, F. Masulli, and S. Rovetta, A survey of kernel and spectral methods for clustering, Pattern Recognition, Vol. 41, pp. 176-190, 2008.
[5] K. S. Chuang, H. L. Tzeng, S. Chen, J. Wu, T. J. Chen, Fuzzy c-means clustering with spatial information for image segmentation, Comput. Med. Imaging Graphics, Vol. 30, pp.9-15, 2006.
[6] J. F. Kolen and T. Hutcheson, Reducing the Time Complexity of the Fuzzy C-Means Algorithm, IEEE Trans. Fuzzy Systems, Vol. 10, pp. 263-267, 2002.
[7] S. Eschrich, J. Ke, L. O. Hall, and D. B. Goldgof, Fast Accurate Fuzzy Clustering Through Data Reduction, IEEE Trans. Fuzzy Systems, pp.262-270, 2003.
[8] T. W. Cheng, D. B. Goldgof, L. O. Hall, Fast fuzzy clustering. Fuzzy Sets Syst., Vol. 93, pp. 49-56, 1998.
[9] 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 Biannual World Automation Congress, 2002.
[10] 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.
[11] H. Y. Li, C. T. Yang, and W. J. Hwang, Efficient VLSI Architecture for Fuzzy C-Means Clustering in Reconfigurable Hardware, Proc. IEEE International conference on Frontier of Computer Science and Technology, p.168-174, 2009.
[12] H. Y. Li, W.J. Hwang and C.Y. Chang, Efficient Fuzzy C-Means Architecture for Image Segmentation, Sensors, Vol. 11, pp. 6697-6718, 2011.
[13] S. Hauck and A. DeHon, Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation, Morgan Kaufmann, USA, 2008.
[14] Altera Corporation, NIOS II Processor Reference Handbook, http://www.altera.com/ literature/ lit-nio2.jsp, 2012.
[15] Altera Corporation, Floating Point Exponent (ALTFP_EXP) Megafunction User Guide, 2008.
[16] Altera Corporation, Stratix III Device Handbook, http://www.altera.com/ literature/ lit-stx3.jsp, 2012
[17] 楊斯閔, ”Kernel-Based Fuzzy c-Means分群演算法硬體架構實現”,碩士論文,國立臺灣師範大學資訊工程學系,民國壹佰年