Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19438
DC FieldValueLanguage
dc.contributor王周珍zh_TW
dc.contributor陳文淵zh_TW
dc.contributor林基源zh_TW
dc.contributor.advisor吳俊霖zh_TW
dc.contributor.author陳彥良zh_TW
dc.contributor.authorChen, Yen-liangen_US
dc.contributor.other中興大學zh_TW
dc.date2008zh_TW
dc.date.accessioned2014-06-06T07:06:47Z-
dc.date.available2014-06-06T07:06:47Z-
dc.identifierU0005-1307200712544200zh_TW
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dc.identifier.urihttp://hdl.handle.net/11455/19438-
dc.description.abstract在特徵點導向(feature-based)的影像定位(image registration)方法中,最大的挑戰就是如何找出在影像間匹配準確度高,並且不受破壞干擾的特徵點(feature point)與其特徵點描述(feature descriptor)。若兩張影像間相對應之特徵點配對可以正確找出,將其座標代入映射函式(mapping function),求出映射係數後,即可將一張影像的座標系統,轉換到另一張影像的座標系統上,而完成影像定位的目標。 尺度不變特徵轉換(Scale Invariant Feature Transform, SIFT)是一個目前廣泛應用且強健的特徵點擷取(feature detection)與特徵點描述(feature descriptor)方法,透過其找出之特徵點,對於位移、旋轉、縮放、亮度差異以及雜訊等皆能克服並匹配。但若來源影像之亮度差異範圍過大,則特徵匹配結果將失敗。本篇論文提出了使用中位值門檻位元圖(Median Threshold Bitmap , MTB),改善了來源影像亮度差異範圍過大問題。若來源影像中存在有相似度高且重複物件聚集的情形,則SIFT法亦無法有效匹配。在此我們提出了將原始特徵向量,加入了計算特徵點周圍正規值(R)與熵值(entropy)所形成的特徵向量,估計其紋理參數,可有效改善重複物件聚集的問題。在匹配速度上,原始SIFT演算法使用計算歐式距離後加以排序的方式,求得最小歐式距離為其匹配點,排序的過程中,浪費過多的時間。我們提出了使用部份和(partial sum)演算法加以改善,在特徵點數量大的情況下,可加快其計算時間至原始方法的40%。實驗結果顯示,所提演算法能有效解決影像亮度差異範圍過大與影像相似物件聚集之問題,並加速其匹配速度,達到影像定位的目的。zh_TW
dc.description.abstractIn feature-based image registration method, the most challenge is to find the features which have high matching accuracy between images. If the pairs of feature can be found correctly between images, the mapping function is constructed. It should transform one image to overlay it over the other one. Scale Invariant Feature Transform (SIFT) is a robust feature transform for feature detection and descriptor. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in viewpoint, addition of noise and change in illumination. But if the source images have large change in illumination and the existence of similar objects, the matching will fail. In matching time, SIFT spends too much time on sorting. In this thesis, we use the median threshold bitmap (MTB) to solve the large change in illumination problem and estimate the texture parameter with normalized (R) and entropy (E) to improve the existence of similar objects. In feature matching, we use partial sum to improve matching time. The experimental results show that the proposed method can improve and solve the problems above effectively and shorten the matching time.en_US
dc.description.tableofcontents第一章 前言 1 1.1研究背景 1 1.2研究動機與目的 2 1.3研究架構 4 第二章 文獻回顧 5 2.1映射函式與轉換 5 2.2區域導向影像定位法 9 2.3特徵導向影像定位法 15 2.4尺度不變特徵轉換 17 第三章 所提之影像定位演算法 25 第四章 實驗結果與討論 33 4.1不同曝光時間影像之特徵匹配實驗結果 33 4.2相似物件聚集實驗結果與參數設定 40 4.3所提演算法之影像定位實驗結果 46 第五章 結論與未來展望 49 參考文獻 50zh_TW
dc.language.isoen_USzh_TW
dc.publisher資訊科學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1307200712544200en_US
dc.subjectImage registrationen_US
dc.subject影像定位zh_TW
dc.subjectFeature detectionen_US
dc.subjectFeature descriptoren_US
dc.subjectPartial sumen_US
dc.subjectSIFTen_US
dc.subjectMTBen_US
dc.subject特徵擷取zh_TW
dc.subject特徵描述zh_TW
dc.subject部份和zh_TW
dc.subjectSIFTzh_TW
dc.subjectMTBzh_TW
dc.title一個不同曝光時間影像序列之強健特徵導向影像定位法zh_TW
dc.titleA Robust Feature-Based Registration Method for Differently Exposed Image Sequencesen_US
dc.typeThesis and Dissertationzh_TW
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