【摘要】：影像匹配作為圖像處理領(lǐng)域的重要技術(shù)之一，是當前攝影測量學(xué)與計算機視覺領(lǐng)域的重要課題，它被廣泛應(yīng)用于礦產(chǎn)研究、醫(yī)用人體影像配準、個人指紋識別、雷達跟蹤監(jiān)測、無人機導(dǎo)航探測等領(lǐng)域。現(xiàn)階段影像匹配的研究越來越多，它的精度已經(jīng)達不到許多工作的要求，最小二乘影像匹配由于它的高精度受到廣泛關(guān)注。 本文是在參與研發(fā)地面攝影測量系統(tǒng)的最小二乘影像匹配的模塊的情況下，通過研究核線影像生成、點特征提取、影像匹配和最小二乘影像匹配的各種算法，采用相關(guān)系數(shù)算法實現(xiàn)最小二乘影像匹配，使得影像可以達到子像素級。 本文根據(jù)原始影像的特點，選擇了基于數(shù)字糾正的核線影像生成方法，研究了點特征提取算法以便于提供影像匹配的數(shù)據(jù)，在論文中分析了常用的Harris算子、Moravec算子和Forstner算子并進行比較，分析各個算子的優(yōu)點和缺點，選取Harris算子提取特征點來作為影像匹配數(shù)據(jù)源。此外，在使用相關(guān)系數(shù)算法進行影像匹配和最小二乘影像匹配時，，分析了影像的紋理信息、搜索窗口的大小和閾值的選擇對于匹配結(jié)果的影響，盡可能使得影像的匹配效率更高，匹配結(jié)果更好。
[Abstract]:Image matching, as one of the most important techniques in the field of image processing, is an important subject in the field of photogrammetry and computer vision. It is widely used in mineral research, medical human body image registration, personal fingerprint identification, radar tracking and monitoring. UAV navigation and detection and other fields. At present, there are more and more research on image matching, and its precision has not reached the requirements of many work. Because of its high accuracy, the least square image matching has been paid more and more attention. This paper is involved in the research and development of the least square image matching module of ground photogrammetry system, through the study of kernel line image generation, point feature extraction, image matching and least square image matching algorithms. The correlation coefficient algorithm is used to realize the least square image matching, so that the image can reach sub-pixel level. According to the characteristics of the original image, this paper chooses the kernel line image generation method based on digital correction, and studies the point feature extraction algorithm in order to provide image matching data. In this paper, the common Harris operator Moravec operator and Forstner operator are analyzed and compared, and the advantages and disadvantages of each operator are analyzed. Harris operator is selected to extract feature points as image matching data source. In addition, when using correlation coefficient algorithm for image matching and least square image matching, the texture information of image, the influence of the size of search window and the selection of threshold value on the matching results are analyzed. As far as possible, the image matching efficiency is higher and the matching result is better.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：P231.5

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本文編號：2134154