基于圖理論的圖像特征匹配算法研究
發(fā)布時(shí)間：2018-09-11 07:00
【摘要】：圖像匹配是指將不同時(shí)間、不同成像條件下獲取的的兩幅或多幅圖像進(jìn)行空間上的對(duì)準(zhǔn),確定圖像之間的幾何映射關(guān)系,進(jìn)而使得圖像能夠匹配的過程。作為計(jì)算機(jī)視覺的核心技術(shù)之一,圖像匹配是圖像分析與處理中的基礎(chǔ)問題。圖像匹配在目標(biāo)對(duì)象識(shí)別、紋理發(fā)現(xiàn)與分析、圖像信息融合、圖像檢索等領(lǐng)域的應(yīng)用越來越廣泛,具有十分重要的研究意義�；谔卣鞯膱D像匹配算法由于對(duì)圖像的尺度變化、仿射形變等具有良好的穩(wěn)定性和魯棒性,受到了國(guó)內(nèi)外學(xué)者的廣泛關(guān)注。圖模型作為一種描述數(shù)據(jù)的工具,可以有效的表示圖像的結(jié)構(gòu)特征,同時(shí)保留區(qū)域之間的相互聯(lián)系,利用圖模型來實(shí)現(xiàn)圖像特征點(diǎn)匹配的研究受到了學(xué)術(shù)界的青睞�；趫D理論的圖像特征點(diǎn)匹配方法,由于具有較好的適應(yīng)性和較高的匹配精度,是近年來研究的熱點(diǎn)和難點(diǎn)問題。本文圍繞基于圖理論的圖像特征匹配方法進(jìn)行了相關(guān)研究,主要研究?jī)?nèi)容和研究成果如下:(1)研究分析了圖像匹配的理論意義和實(shí)用價(jià)值,對(duì)國(guó)內(nèi)外關(guān)于圖像匹配的研究現(xiàn)狀進(jìn)行了概括和總結(jié)。重點(diǎn)對(duì)圖像特征匹配進(jìn)行了理論方面的概述,首先重點(diǎn)介紹了圖的基本概念和矩陣表示,然后介紹了圖像特征匹配中的兩個(gè)關(guān)鍵技術(shù):特征提取和特征描述,最后對(duì)經(jīng)典的SIFT圖像特征匹配算法進(jìn)行了妔}0的分析。圖的相關(guān)理論和對(duì)SIFT算法的研究,為本文圖像匹配算法的提出奠定了重要的理論基礎(chǔ)。(2)針對(duì)圖像特征點(diǎn)匹配,結(jié)合層次聚類的思想,本文給出了一種基于自頂向下分裂聚類的圖像匹配算法。該算法的主要思想是采用互k近鄰圖模型來表示圖像之間的對(duì)應(yīng)關(guān)系,在互κ近鄰圖表示模型中,頂點(diǎn)代表特征點(diǎn)之間的對(duì)應(yīng)關(guān)系,頂點(diǎn)之間的邊代表對(duì)應(yīng)關(guān)系的幾何相容性。定義的團(tuán)密度函數(shù)可以衡量是否屬于同一個(gè)團(tuán),一般情況下,團(tuán)密度的值越大,越有可能是正確的團(tuán)。該算法不僅可以獲得圖像之間的對(duì)應(yīng)關(guān)系,還可以指示出哪些對(duì)應(yīng)關(guān)系屬于同一個(gè)目標(biāo)。同一個(gè)團(tuán)內(nèi)的對(duì)應(yīng)關(guān)系之間幾何相容性較高,不同團(tuán)之間的對(duì)應(yīng)關(guān)系相容性則較低,因此不同的目標(biāo)會(huì)呈現(xiàn)出不同的團(tuán)。在互k近鄰圖表示模型的基礎(chǔ)上,通過團(tuán)檢測(cè)方法獲得圖中的團(tuán),利用的是分裂聚類的思想。最終,根據(jù)團(tuán)內(nèi)包含的頂點(diǎn)恢復(fù)出團(tuán)內(nèi)的對(duì)應(yīng)關(guān)系,從而達(dá)到圖像匹配的目的。在真實(shí)圖像上的對(duì)比實(shí)驗(yàn)表明,自頂向下分裂聚類的圖像匹配算法在匹配性能上要優(yōu)于ACC算法,提高了圖像匹配的查全率和查準(zhǔn)率,實(shí)驗(yàn)的效果圖和定量分析結(jié)果都表明該算法具有較好的匹配結(jié)果。(3)為了進(jìn)一步提高圖像特征匹配算法的準(zhǔn)確度,本文提出了一種基于局部近鄰圖的特征描述與特征匹配算法,通過為每個(gè)特征點(diǎn)構(gòu)建局部近鄰圖來深層次挖掘圖像上的結(jié)構(gòu)信息。該算法首先通過FAST和SURT算法檢測(cè)初始的特征點(diǎn),然后為所有的特征點(diǎn)構(gòu)造局部近鄰圖,每個(gè)局部圖由該特征點(diǎn)及其近鄰特征點(diǎn)組成,至此形成一種新穎的特征描述方法。在這個(gè)新穎的特征描述符的基礎(chǔ)上,給出了一個(gè)相似性度量函數(shù)和一個(gè)能量函數(shù),鑒于此,提出了一種基于局部近鄰圖模型的特征匹配算法。為了驗(yàn)證該算法的有效性,進(jìn)行了兩個(gè)方面的實(shí)驗(yàn):高斯噪聲模擬實(shí)驗(yàn)和真實(shí)圖像匹配實(shí)驗(yàn)。高斯噪聲模擬實(shí)驗(yàn)的目的是為了分析離群點(diǎn)和變形噪聲對(duì)算法性能的影響,而在真實(shí)圖像庫(kù)上進(jìn)行實(shí)驗(yàn),是為了驗(yàn)證該算法在圖像特征匹配中的準(zhǔn)確度。實(shí)驗(yàn)的實(shí)例圖和定量分析結(jié)果表明,基于局部近鄰圖的特征匹配算法較SM算法具有一定的優(yōu)越性。
[Abstract]:Image matching refers to the process of spatial alignment of two or more images acquired under different imaging conditions at different times, to determine the geometric mapping relationship between images, and then to make the image matching. As one of the core technologies of computer vision, image matching is a basic problem in image analysis and processing. Matching is more and more widely used in object recognition, texture discovery and analysis, image information fusion, image retrieval and other fields. Feature-based image matching algorithm has been widely used by scholars at home and abroad because of its good stability and robustness to image scale change, affine deformation and so on. Graph model, as a tool for describing data, can effectively represent the structural features of an image while preserving the relationship between regions. The research on feature point matching based on graph model is favored by academia. High matching accuracy is a hot and difficult problem in recent years. This paper focuses on the image feature matching method based on graph theory. The main research contents and achievements are as follows: (1) The theoretical significance and practical value of image matching are analyzed, and the research status of image matching at home and abroad is summarized. In the end, the classical SIFT image feature matching algorithm is analyzed by_} 0. The related theory of graph is analyzed. The research of SIFT algorithm has laid an important theoretical foundation for the proposed image matching algorithm. (2) Aiming at image feature point matching and combining the idea of hierarchical clustering, this paper presents an image matching algorithm based on top-down splitting clustering. In the mutual kappa nearest neighbor graph representation model, vertices represent correspondence between feature points and edges between vertices represent geometric compatibility of correspondence. The defined clique density function can be used to measure whether a clique belongs to the same clique. In general, the larger the clique density, the more likely the clique is to be correct. The correspondence between images can also indicate which correspondence belongs to the same target. The geometric compatibility of correspondence in the same clique is higher, but the correspondence between different cliques is lower. Therefore, different targets will present different cliques. Methods The clique in the graph was obtained by using the idea of split clustering. Finally, the corresponding relationship in the clique was recovered according to the vertices contained in the clique, so as to achieve the purpose of image matching. (3) To further improve the accuracy of image feature matching algorithm, a feature description and feature matching algorithm based on local nearest neighbor graph is proposed, which constructs local nearest neighbor graph for each feature point. The algorithm first detects the initial feature points by FAST and SURT algorithm, and then constructs a local neighborhood graph for all the feature points. Each local graph consists of the feature points and their neighborhood feature points, thus forming a novel feature description method. Based on this, a similarity measure function and an energy function are given. In view of this, a feature matching algorithm based on local nearest neighbor graph model is proposed. In order to verify the effectiveness of the algorithm, two experiments are carried out: Gaussian noise simulation experiment and real image matching experiment. The effect of outliers and distortion noise on the performance of the algorithm is analyzed, and experiments on real image database are carried out to verify the accuracy of the algorithm in image feature matching.The experimental results show that the feature matching algorithm based on local nearest neighbor graph is superior to SM algorithm.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41
本文編號(hào)：2235952
[Abstract]:Image matching refers to the process of spatial alignment of two or more images acquired under different imaging conditions at different times, to determine the geometric mapping relationship between images, and then to make the image matching. As one of the core technologies of computer vision, image matching is a basic problem in image analysis and processing. Matching is more and more widely used in object recognition, texture discovery and analysis, image information fusion, image retrieval and other fields. Feature-based image matching algorithm has been widely used by scholars at home and abroad because of its good stability and robustness to image scale change, affine deformation and so on. Graph model, as a tool for describing data, can effectively represent the structural features of an image while preserving the relationship between regions. The research on feature point matching based on graph model is favored by academia. High matching accuracy is a hot and difficult problem in recent years. This paper focuses on the image feature matching method based on graph theory. The main research contents and achievements are as follows: (1) The theoretical significance and practical value of image matching are analyzed, and the research status of image matching at home and abroad is summarized. In the end, the classical SIFT image feature matching algorithm is analyzed by_} 0. The related theory of graph is analyzed. The research of SIFT algorithm has laid an important theoretical foundation for the proposed image matching algorithm. (2) Aiming at image feature point matching and combining the idea of hierarchical clustering, this paper presents an image matching algorithm based on top-down splitting clustering. In the mutual kappa nearest neighbor graph representation model, vertices represent correspondence between feature points and edges between vertices represent geometric compatibility of correspondence. The defined clique density function can be used to measure whether a clique belongs to the same clique. In general, the larger the clique density, the more likely the clique is to be correct. The correspondence between images can also indicate which correspondence belongs to the same target. The geometric compatibility of correspondence in the same clique is higher, but the correspondence between different cliques is lower. Therefore, different targets will present different cliques. Methods The clique in the graph was obtained by using the idea of split clustering. Finally, the corresponding relationship in the clique was recovered according to the vertices contained in the clique, so as to achieve the purpose of image matching. (3) To further improve the accuracy of image feature matching algorithm, a feature description and feature matching algorithm based on local nearest neighbor graph is proposed, which constructs local nearest neighbor graph for each feature point. The algorithm first detects the initial feature points by FAST and SURT algorithm, and then constructs a local neighborhood graph for all the feature points. Each local graph consists of the feature points and their neighborhood feature points, thus forming a novel feature description method. Based on this, a similarity measure function and an energy function are given. In view of this, a feature matching algorithm based on local nearest neighbor graph model is proposed. In order to verify the effectiveness of the algorithm, two experiments are carried out: Gaussian noise simulation experiment and real image matching experiment. The effect of outliers and distortion noise on the performance of the algorithm is analyzed, and experiments on real image database are carried out to verify the accuracy of the algorithm in image feature matching.The experimental results show that the feature matching algorithm based on local nearest neighbor graph is superior to SM algorithm.
【學(xué)位授予單位】：安徽大學(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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