近年來，生物啟發式演算技術已應用在許多領域上，如圖像處理、神經網路與模式識別。本論文採用在生物啟發式演算中一項較新的技術，名為混合青蛙跳演算法，並應用在影像分割與支援向量機的訓練過程。其實驗結果驗證了使用混合蛙跳演算法於影像的多閥值分割處理上可獲得有效的結果，以及在支援向量機的架構中能提供更高的分類確率。

In recent years the technology of bio-inspired computation have been applied to many fields such as the image processing, neural network and pattern recognition. The shuffled frog-leaping algorithm is a new method among the bio-inspired computation. This thesis applied this method in image segmentation and the training of support vector machine. Experimental results showed that the proposed method can effectively segment the image in multilevel thresholding and can construct the support vector machine in high correct classification rate.

摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
CHAPTER 1 1
緒論 1
1.1 研究動機與目的 1
1.1.1 影像多閥值分割 1
1.1.2 支援向量機 2
1.2 文獻探討 3
1.2.1 影像閥值 3
1.2.2 支援向量機的應用與改進 4
1.3 研究方法簡介 5
1.3.1 演化式運算 5
1.3.2 混合蛙跳演算法 6
1.3.3 改良式混合蛙跳演算法 9
1.4 論文結構簡介 9
CHAPTER 2 10
運用混合蛙跳演算法於影像多閥值分割 10
2.1 簡介 10
2.2 熵值最大化與影像多閥值優化 10
2.3 混合蛙跳演算法應用於影像多閥值優化 11
CHAPTER 3 14
運用混合蛙跳演算法於非線性支援向量機之參數優化 14
3.1 簡介 14
3.2 線性支援向量機 14
3.3 非線性支援向量機 16
3.4 混合蛙跳演算之參數優化 18
3.4.1 實驗設計 18
3.4.2 實驗流程 18
CHAPTER 4 21
實驗結果 21
4.1 研究環境簡介 21
4.2 影像多閥值優化實驗結果 21
4.3 支援向量機之參數優化實驗結果 52
CHAPTER 5 53
研究結論與未來展望 53
參考文獻 54

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