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研究生:陳 毅
研究生(外文):Chen, Yi
論文名稱:將格子波爾茲曼方法應用於核心模糊能量動態輪廓的影像分割
論文名稱(外文):Kernel Fuzzy Energy Active Contour Using Lattice Boltzmann Method In Image Segmentation
指導教授:許靖涵
指導教授(外文):Hsu, Ching-Han
口試委員:徐泳欽羅世瑋
口試委員(外文):Hsu, Yung-ChinLo, Shih-Wei
口試日期:2017-07-31
學位類別:碩士
校院名稱:國立清華大學
系所名稱:生醫工程與環境科學系
學門:工程學門
學類:生醫工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:64
中文關鍵詞:動態輪廓模型模糊分群法格子波爾茲曼方法水平集方法核心
外文關鍵詞:active contour modelfuzzy clusteringlattice boltzmann methodlevel set methodkernel
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快速且精確的影像分割一直都是電腦視覺領域中很大的挑戰,原因在於自然和醫學影像普遍受到雜訊以及像素強度不均的干擾,為了克服這些因素,模糊分群法被大量應用在影像分割中,尤其在加入核心概念後,能夠有效地處理影像上的雜訊及異值點,並透過水平集函數的實作,使影像上的曲線能夠收斂到物體的輪廓,但是水平集函數的計算複雜度較高,也需要透過再初始化或能量限制項維持函數的平滑程度及穩定性。
本研究中提出演算法架構是將格子波爾茲曼方法應用在解開核心模糊分群的水平集函數中,格子波爾茲曼方法透過每個像素獨立運算,獲得水平集函數迭代式的解答,並同時維持水平集函數的平滑性質,這也代表著能夠高度地被圖形處理器平行化,因此這個演算法能提供快速、穩定並且不易受到初始輪廓影像的準確結果,我們在實驗中,藉由合成和自然影像的分割結果,證實了演算法的穩定以及準確度,並透過圖形處理器的執行時間,展現演算法的效率。
Fast and accurate image segmentation is still a challenging task in computer vision because real-world images are often distorted by noise and intensity inhomogeneity. In order to overcome these problems, fuzzy clustering is extensively applied to image segmentation because of the strong ability to reject local minimum. It also incorporates with kernel metrics to enhance robustness against noise and outliers and construct a nonlinear energy function based on a variational level set framework. However, level set implementation often costs a lot of CPU times, and needs “re-initialization”or regularizing terms to keep level set function smooth and stable.
Our research provides the algorithm which solve level set equation of kernel fuzzy active contour model by using Lattice Boltzmann Method solver. Lattice Boltzmann Method can recover the level set PDE by computing pixel-by-pixel independently and maintain the stability and smoothness of level set function in the meanwhile. Therefore, this algorithm is fast, stable and independent to the position of the initial curve. Experiments on synthetic and real-world images demonstrate the stability and performance of the proposed method, and also show the efficiency of algorithm by using graphics processing unit.
1 前言 p.6
2 分群法 (Clustering) p.9
2.1 K-平均分群法 (K-means clustering) p.9
2.2 模糊C-平均分群法 (Fuzzy C-means clustering, FCM) p.10
2.3 調整後的模糊C-平均分群法 (Modified Fuzzy C-means clustering) p.11
3 動態輪廓模型 (Active Contour Model) p.16
3.1 邊界型 (Edge-based) p.16
3.2 區域型 (Region-based) p.17
3.2.1 Chan-Vese模型 p.17
3.2.2 調整後的Chan-Vese模型 p.19
3.3 水平集方法 (Level Set Method) p.23
4 格子波爾茲曼方法 (Lattice Boltzmann Method, LBM) p.28
4.1 圖形處理器 (Graphic Processing Unit, GPU)平行化 p.28
4.2 用格子波爾茲曼方法解偏微分方程 (Lattice Boltzmann Method for PDE Solver) p.29
5 核心模糊能量結合格子波爾茲曼方法 (Kernel Fuzzy Energy Lattice Boltzmann Method, KFELBM) p.33
5.1 構築模型(Model Construction) p.33
5.2 最小化能量 (Energy Minimization) p.36
5.3 選擇參數 (Parameter Selection) p.38
6 實驗結果與討論 p.41
7 結論與未來工作 p.55
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