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研究生: 黃紹慈
Huang, Shao-Tzu
論文名稱: 基於K-Means分群改良高解析度特徵描述子之匹配演算法
K-Means Based Matching Algorithm for Multi-Resolution Feature Descriptors
指導教授: 許陳鑑
Hsu, Chen-Chien
王偉彥
Wang, Wei-Yen
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2017
畢業學年度: 105
語文別: 中文
論文頁數: 62
中文關鍵詞: 線性搜尋法K-means分群特徵點匹配
英文關鍵詞: feature matching, K-means clustering, linear exhaustive search
DOI URL: https://doi.org/10.6345/NTNU202202695
論文種類: 學術論文
相關次數: 點閱:99下載:8
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  • 匹配兩張影像之高維度特徵點,是在電腦視覺領域的眾多應用中花費大量計算資源的一環。雖然透過降低特徵點維度的手段得以抑制計算量,但是會因而犧牲了匹配的精準性。因此,本文提出一改良式的影像匹配演算法,運用K-means分群的特性,不僅可以有效地降低匹配所需的運算時間,同時也保有了一定程度的精準性。實驗結果顯示,與參考的文獻方法相較,本文所提出的方法在精準度上較具優勢。另外,為提升演算法的執行效能,本文也利用FPGA實現所提出之影像匹配演算法,藉由管線式的硬體設計架構,進一步提升影像匹配的速度。

    Matching high dimensional features between images is computationally expensive for exhaustive search approaches in computer vision. Although the dimension of the feature can be degraded by simplifying the prior knowledge of homography, matching accuracy may degrade as a result. In this thesis, we present a feature matching method based on K-means algorithm, which combines with L1-norm based pyramid structure that reduces the matching cost to match the features between images instead of using a simplified geometric assumption. Experimental results show that the proposed method outperforms the previous linear exhaustive search approaches in terms of the inlier ratio of matched pairs. We also implement the proposed approach on FPGA using a structured pipeline design to further improve the execution efficiency of the proposed matching algorithm.

    摘 要 i ABSTRAC ii 誌 謝 iii 表目錄 vi 圖目錄 vii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 論文架構 4 第二章 SIFT演算法 5 2.1 建立影像金字塔 6 2.2 特徵點偵測 7 2.3 賦予特徵點主方向 12 2.4 特徵點描述 13 第三章 基於K-means分群改良高解析度特徵描述子之匹配演算法 16 3.1 數值金字塔的建立 17 3.2 K-Means分群演算法 18 3.3 叢聚匹配 20 3.4 叢聚組內之匹配門檻值計算 21 3.5 以數值金字塔為基礎之匹配演算 21 第四章 匹配效能的實驗分析與結果 24 4.1 分群效度指標 24 4.2 隨機抽樣一致(RANSAC) 27 4.3 仿射轉換 35 第五章 匹配演算法的硬體實現 45 5.1 匹配演算法的硬體架構 45 5.2 匹配模組 46 5.2.1 降維模組 47 5.2.2 距離計算模組 48 5.2.3 門檻值計算模組 50 5.2.4 匹配篩檢模組 52 5.2.5 模組資訊 52 5.2.6 匹配模組之驗證 53 第六章 結論 55 6.1 結論 55 參考文獻 56 自傳 60 學術成就 62

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