【摘要】：隨著數(shù)字圖像處理和計(jì)算機(jī)視覺技術(shù)的發(fā)展,圖像配準(zhǔn)技術(shù)已成為圖像處理領(lǐng)域極為重要的一項(xiàng)技術(shù),被廣泛應(yīng)用于遙感、軍事、醫(yī)學(xué)圖像分析,機(jī)器視覺和模式識(shí)別等許多重要領(lǐng)域。近年來,諸多國(guó)內(nèi)外學(xué)者圍繞圖像配準(zhǔn)進(jìn)行了深入研究,提出了多種高效的圖像配準(zhǔn)方法。其中最具經(jīng)典的是Lowe提出的尺度不變特征變換(Scale Invariant Feature Transform,SIFT),SIFT特征在圖像旋轉(zhuǎn)、角度變換、仿射變換和尺度縮放條件下都保持良好的不變性。特征匹配作為圖像配準(zhǔn)過程中重要的一部分,一直以來都是學(xué)者們關(guān)注和研究的重點(diǎn)。本文的主要研究工作是基于改進(jìn)SIFT算法的圖像配準(zhǔn),旨在實(shí)現(xiàn)特征點(diǎn)精確搜索和有效匹配,本論文的主要內(nèi)容如下:(1)詳細(xì)介紹了圖像配準(zhǔn)的研究背景和國(guó)內(nèi)外研究現(xiàn)狀,給出了圖像配準(zhǔn)的原理和數(shù)學(xué)理論基礎(chǔ)。介紹了幾種常見的幾何變換模型,為后續(xù)圖像配準(zhǔn)提供理論基礎(chǔ)。(2)詳細(xì)論述了SIFT算法原理,針對(duì)原SIFT算法在圖像過程中存在誤匹配、漏掉大量的正確匹配對(duì)以及正確匹配率低的問題,本文提出了一種基于尺度、方向和距離約束的改進(jìn)的SIFT匹配算法,通過對(duì)特征點(diǎn)添加約束因子,剔除誤匹配對(duì)。實(shí)驗(yàn)結(jié)果表明,本文方法能夠增加正確匹配對(duì)數(shù)量并提高正確匹配率。(3)針對(duì)SIFT算法在遙感圖像配準(zhǔn)過程中,匹配對(duì)數(shù)量較少,正確匹配率低的問題,本文提出了一種基于局部仿射約束改進(jìn)的SIFT遙感圖像配準(zhǔn)方法。給出了一種新的梯度算子,使用圓形鄰域代替原SIFT算法中的方形鄰域?qū)μ卣鼽c(diǎn)構(gòu)造特征描述子,通過FSC(Fast Sample Consensus)算法和仿射變換局部區(qū)域搜索算法,實(shí)現(xiàn)特征點(diǎn)的匹配。實(shí)驗(yàn)結(jié)果表明,本文方法對(duì)遙感圖像配準(zhǔn)效果較好,正確匹配率和配準(zhǔn)精度均有所提高。
[Abstract]:With the development of digital image processing and computer vision technology, image registration technology has become an extremely important technology in the field of image processing, and has been widely used in remote sensing, military, medical image analysis. There are many important fields such as machine vision and pattern recognition. In recent years, many domestic and foreign scholars have carried on the thorough research around the image registration, proposed many kinds of efficient image registration methods. The most classical one is the scale invariant feature transformation proposed by Lowe. The (Scale Invariant Feature Transform,SIFT), SIFT features are invariant under the conditions of image rotation angle transformation affine transformation and scale scaling. As an important part of image registration, feature matching has always been the focus of attention and research. The main research work of this paper is based on the improved SIFT algorithm for image registration, aiming at the accurate search of feature points and effective matching. The main contents of this paper are as follows: (1) the research background of image registration and the current research situation at home and abroad are introduced in detail. The principle and mathematical theory of image registration are given. Several common geometric transformation models are introduced, which provide a theoretical basis for the subsequent image registration. (2) the principle of the SIFT algorithm is discussed in detail, aiming at the mismatch of the original SIFT algorithm in the image process. This paper presents an improved SIFT matching algorithm based on scaling, direction and distance constraints. By adding constraint factors to the feature points, the mismatch pairs are eliminated. Experimental results show that this method can increase the number of correct matching pairs and improve the correct matching rate. (3) in the process of remote sensing image registration, SIFT algorithm has fewer matching pairs and lower correct matching rate. In this paper, an improved SIFT remote sensing image registration method based on local affine constraints is proposed. In this paper, a new gradient operator is proposed, which uses circular neighborhood instead of square neighborhood in the original SIFT algorithm to construct feature descriptors. The matching of feature points is realized by FSC (Fast Sample Consensus) algorithm and affine transform local region search algorithm. The experimental results show that the proposed method is effective for remote sensing image registration, and the correct matching rate and registration accuracy are improved.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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本文編號(hào)：2275551