臺灣博碩士論文加值系統

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

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