Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19438
標題: 一個不同曝光時間影像序列之強健特徵導向影像定位法
A Robust Feature-Based Registration Method for Differently Exposed Image Sequences
作者: 陳彥良
Chen, Yen-liang
關鍵字: Image registration
影像定位
Feature detection
Feature descriptor
Partial sum
SIFT
MTB
特徵擷取
特徵描述
部份和
SIFT
MTB
出版社: 資訊科學系所
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摘要: 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。
In 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.
URI: http://hdl.handle.net/11455/19438
其他識別: U0005-1307200712544200
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1307200712544200
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