【摘要】：圖像匹配技術(shù)是將不同視角、不同傳感器、不同時(shí)間下獲取的具有一定重疊部分的兩幅影像進(jìn)行匹配，它是圖像處理的一個(gè)基本問題。圖像匹配方法大致可以分為兩類：一、基于灰度的圖像匹配方法，二、基于特征的圖像匹配方法。本文總結(jié)介紹了前人在圖像匹配領(lǐng)域的研究成果及現(xiàn)狀，詳細(xì)介紹了圖像匹配的流程，并且提出了極大似然估計(jì)（MLESAC）算法用于非常規(guī)攝影條件下立體像對(duì)的同名點(diǎn)匹配當(dāng)中。 在研究什么是非常規(guī)攝影條件下，介紹了沿主光軸方向的攝影，以及大轉(zhuǎn)角的攝影，，此類情況下均是非常規(guī)攝影。本文詳細(xì)介紹了，沿主光軸方向的攝影，畫出了其立體像對(duì)的成像模型，也畫了常規(guī)攝影中立體像對(duì)的成像模型，從圖中可以看出沿主光軸方向的攝影的視差、核線排列等問題與傳統(tǒng)的常規(guī)攝影測(cè)量有很大的區(qū)別。 在研究基于特征的圖像匹配方法時(shí)，本文提出了基于SIFT（尺度不變特征轉(zhuǎn)換）算法并加入極大似然估計(jì)(MLESAC)算法用于非常規(guī)攝影條件下立體像對(duì)的同名點(diǎn)的匹配,用SIFT提取特征點(diǎn)，再用基于似然函數(shù)值的極大似然估計(jì)算法(MLESAC)去除誤匹配的特征點(diǎn)對(duì)，實(shí)現(xiàn)圖像的精確匹配。并用隨機(jī)采樣一致性算法(RANSAC)對(duì)SIFT提取的特征點(diǎn)對(duì)進(jìn)行處理對(duì)比分析了兩種方法處理后的結(jié)果。實(shí)驗(yàn)驗(yàn)證，MLESAC算法應(yīng)用于非常規(guī)攝影條件下立體像對(duì)的同名點(diǎn)匹配是高效穩(wěn)定的圖像匹配。 最后對(duì)本文所做的工作進(jìn)行了總結(jié)，并對(duì)本文在圖像匹配領(lǐng)域有哪些需要深入研究的地方進(jìn)行了展望。
[Abstract]:Image matching is a basic problem in image processing, which is to match two images with overlapped parts obtained from different angles of view, different sensors and different time. Image matching methods can be divided into two categories: one is gray-based image matching method, the other is feature-based image matching method. This paper summarizes and introduces the research achievements and current situation of previous researches in image matching, introduces the flow of image matching in detail, and proposes a maximum likelihood estimation (MLESAC) algorithm for matching stereo pairs with the same name under unconventional photography conditions. Under the condition of studying what is unconventional photography, this paper introduces the photography along the main optical axis, as well as the photography with large rotation angle, in which case the photography is unconventional. This paper introduces in detail the imaging model of the stereo image pair along the main optical axis and the imaging model of the stereo image pair in the conventional photography. The parallax of the photography along the main optical axis can be seen from the picture. Nuclear alignment and other problems are quite different from conventional photogrammetry. When studying the feature-based image matching method, this paper proposes an algorithm based on SIFT (Scale-Invariant feature Transformation) and adds the maximum likelihood estimation (MLESAC) algorithm to match the points of the same name of stereo image pair under the condition of unconventional photography. The feature points are extracted by SIFT. Then the maximum likelihood estimation algorithm (MLESAC) based on the likelihood function is used to remove the mismatched feature pairs to realize the accurate image matching. A random sampling consistency algorithm (RANSAC) is used to deal with the feature pairs extracted by SIFT. The results of the two methods are compared and analyzed. Experimental results show that the MLESAC algorithm is an efficient and stable method for stereo image pair matching with the same name. Finally, the paper summarizes the work done in this paper, and looks forward to what needs to be further studied in the field of image matching in this paper.
【學(xué)位授予單位】：中國(guó)地質(zhì)大學(xué)（北京）
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP391.41;P23

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本文編號(hào)：2233517