粒子群優化與二維Otsu演算法於影像二元化閥值選取研究

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姓名

張瑞芳(Juei-Feng Chang) 查詢紙本館藏

畢業系所

通訊工程學系在職專班

論文名稱

粒子群優化與二維Otsu演算法於影像二元化閥值選取研究
(Image Threshold selection based on PSO and 2D-Otsu Algorithm)

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摘要(中)

圖像分割中的閥值選取是一個相當重要的技術，也延伸到許多領域當中，如特徵辨識、生物醫學影像等等，而選取的方法包含許多，P參數法、最大熵閥值法、Otsu法等都是可以作為選取閥值的方法。閥值選取基本上是一個像素分佈的問題，基於可以依像素特性將圖像分為兩類:一個是屬於目標部份，另一個則是背景部分，判斷的依據則為像素的灰階數值小於或等於閥值分為一類，像素的灰階數值大於閥值分為一類，此一技術已被提出且廣泛使用。針對Otsu法的延伸-二維的Otsu法(2D-Otsu)可以在當灰階直方圖並未具有雙峰值的特性存在條件下，得到一個較好的分割閥值，將目標及背景區隔開來，但須經過較複雜的計算過程。而PSO演算法是一個人工智慧演算法，具有參數少，收斂速度快等特性，可以成功地結合二維的Otsu法加速搜尋圖像分割閥值。

摘要(英)

Threshold selecting is a significant technique for image segmentation, which is applied broadly in many fields such as character recognition, analysis of biologic images etc. The method mainly includes P-tile method , the maximum entropy method , Otsu and so on. It is essentially a pixels classification problem. Its basic objective is to classify the pixels of a given image into two classes: one is those pertaining to an object and another is those pertaining to the background. While one includes pixels with gray values that are below or equal to a certain threshold, the other includes those with gray values above the threshold. As an extension of Otsu algorithm, two-dimensional Otsu algorithm (2D-Otsu) can give good result for those objects whose histogram does not have two peaks which represent objects and background, however, it costs complex computation. Particle swarm optimization (PSO) is a swarm intelligence optimization algorithm as a set few parameters, better global search capability, search results more stable and widely used. So we combined successfully these two algorithms to get ideal segmentation result with less
computation cost.

關鍵字(中)

★ 圖像分割
★ Otsu法

關鍵字(英)

論文目次

目錄
摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
第一章緒論 1
1-1 研究動機 1
1-2 研究架構與流程 2
第二章演算法介紹 4
2-1 PSO 4
2-1-1 PSO演算法概述 4
2-1-2 PSO基本原理 5
2-1-3 PSO演算法方程式 6
2-2 圖像分割技術 12
2-2-1圖像分割定義 12
2-2-2圖像分割法 13
2-3 Otsu演算法 17
2-3-1 Otsu演算法概述 17
2-3-2 一維Otsu演算法方程式 19
2-3-3 二維Otsu演算法方程式 22
第三章 PSO結合二維Otsu之研究 29
3-1 PSO結合二維Otsu之構想 29
3-2 PSO結合二維Otsu之流程 31
第四章 PSO結合二維Otsu演算法應用於圖像分割 34
4-1 二維Otsu演算法應用於圖像閥值分割選取 34
4-2 PSO結合二維Otsu演算法應用於圖像閥值分割選取 36
4-2-1 實驗測試 37
4-2-2 結果分析 51
第五章結論 54
參考文獻 55

參考文獻

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指導教授

賀嘉律

審核日期

2013-3-5

推文