本文選題：圖像配準(zhǔn) + SIFT��； 參考：《江蘇科技大學(xué)》2014年碩士論文

【摘要】：遙感是獲得全球?qū)崟r(shí)動(dòng)態(tài)數(shù)據(jù)的一種最重要的途徑。隨著社會(huì)經(jīng)濟(jì)技術(shù)的發(fā)展，人們對(duì)實(shí)時(shí)動(dòng)態(tài)數(shù)據(jù)的精確性和高效性有了更高層次的要求，以往的圖像配準(zhǔn)技術(shù)已經(jīng)遠(yuǎn)遠(yuǎn)不能滿足人們的需求，因此遙感圖像的配準(zhǔn)技術(shù)亟待提高，從而成為高效化、智能化、快速化的圖像配準(zhǔn)技術(shù)。 傳統(tǒng)的SIFT（Scale-invariant Feature Transform）算法是一種基于特征的配準(zhǔn)方法。SIFT特征匹配算法最初是由David G. Lowe于2004年在總結(jié)了現(xiàn)有的基于不變量技術(shù)的特征檢測方法的基礎(chǔ)上提出的。SIFT算法具有諸多優(yōu)點(diǎn)，能提取穩(wěn)定的特征，也可以處理兩幅圖像之間發(fā)生旋轉(zhuǎn)、平移、視角變換、仿射變換、光照變換等情況下的匹配問題，甚至在某種程度上對(duì)任意角度拍攝的圖像也具備較為穩(wěn)定的特征匹配能力，從而能夠?qū)崿F(xiàn)兩幅差異較大的圖像之間的特征的匹配。因此這種算法很適合在圖像配準(zhǔn)中使用。雖然SIFT算法具有精確度高、匹配能力強(qiáng)等許多優(yōu)點(diǎn)，但是SIFT算法本身復(fù)雜度高，匹配時(shí)間久，尤其對(duì)于數(shù)據(jù)量較大的遙感影像的處理，應(yīng)用SIFT算法時(shí)處理速度會(huì)明顯降低，過于耗時(shí)。因此，將SIFT算法直接應(yīng)用于遙感影像的處理并不實(shí)用，，在實(shí)際生活中也未能使用這種算法處理遙感影像。但是SIFT算法具有其他圖像配準(zhǔn)算法難以媲美的優(yōu)點(diǎn)，有著非常好的應(yīng)用前景。 為此，我們通過大量研究提出了一種基于LDA算法的改進(jìn)方案。即在SIFT算法的特征提取中加入線性鑒別分析方法（Linear DiscriminantAnalysis，LDA），以減少SIFT特征提取的維度。本文對(duì)提出的基于LDA的SIFT算法的特征原理和配準(zhǔn)步驟進(jìn)行了重點(diǎn)敘述。首先利用SIFT算法提取出圖像的特征點(diǎn)向量，然后用線性鑒別分析方法對(duì)其進(jìn)行特征抽取并降維。通過對(duì)遙感圖像、高維自然圖像和單幅人臉圖像進(jìn)行實(shí)驗(yàn)，實(shí)驗(yàn)結(jié)果表明LDA-SIFT算法在保證匹配精度的同時(shí)，實(shí)時(shí)性要優(yōu)于傳統(tǒng)的SIFT算法，其匹配時(shí)間相對(duì)于傳統(tǒng)SIFT算法明顯縮短。此方案既能保持SIFT算法本身的精確度高、匹配能力強(qiáng)的特點(diǎn)，又能提高匹配的效率，實(shí)時(shí)性較強(qiáng)。非常適合應(yīng)用于維數(shù)高的圖像配準(zhǔn)，尤其是對(duì)時(shí)間和精度要求較高的遙感圖像的配準(zhǔn)。 論文的主要研究成果如下： (1)論文分析了SIFT算法的特征原理以及線性鑒別分析方法LDA的理論基礎(chǔ)。并且針對(duì)本文提出的改進(jìn)算法具體原理以及圖像配準(zhǔn)的步驟進(jìn)行描述。利用LDA易于實(shí)現(xiàn)的特點(diǎn)，將其和SIFT算法進(jìn)行結(jié)合，從而實(shí)現(xiàn)全局優(yōu)化，提高算法的可行性。 (2)將改進(jìn)的算法通過編程設(shè)計(jì)，并將其應(yīng)用于遙感圖像配準(zhǔn)、自然圖像配準(zhǔn)和人臉圖像配準(zhǔn)，通過實(shí)驗(yàn)證明，本算法針對(duì)數(shù)據(jù)量較大的遙感圖像進(jìn)行匹配時(shí)，能夠解決傳統(tǒng)算法實(shí)時(shí)性差的問題；針對(duì)自然、人臉圖像進(jìn)行匹配時(shí)，能夠在提高匹配速率的同時(shí)，提高匹配算法的魯棒性。 本文提出的改進(jìn)的SIFT算法，即LDA-SIFT算法既能降低遙感圖像的配準(zhǔn)時(shí)間，又極大的提高了配準(zhǔn)效率，更好的滿足了當(dāng)今社會(huì)人們對(duì)實(shí)時(shí)動(dòng)態(tài)數(shù)據(jù)的精確性和高效性的要求。
[Abstract]:Remote sensing is the most important way to obtain the global real-time dynamic data. With the development of social and economic technology, people have a higher level of requirements for the accuracy and efficiency of real-time dynamic data. The previous image registration technology has been far from meeting the needs of people. Therefore, the registration technology of remote sensing image needs to be improved, so that the registration technology of remote sensing image needs to be improved. It has become an efficient, intelligent and fast image registration technology.
The traditional SIFT (Scale-invariant Feature Transform) algorithm is a feature based registration method based on the.SIFT feature matching algorithm originally proposed by David G. Lowe on the basis of the existing feature detection methods based on the invariant technology in 2004. The algorithm has many advantages, which can extract stable features and can also be used. In the two images, the matching problem of rotation, translation, angle transformation, affine transformation, illumination transformation and so on, and even to some extent, has a more stable feature matching ability for the images taken at any angle, which can achieve the matching of features between two different images. Although the SIFT algorithm has many advantages, such as high accuracy and strong matching ability, the SIFT algorithm itself has a high complexity and long matching time, especially for the processing of remote sensing images with large amount of data, and the processing speed of the application of SIFT algorithm will be significantly reduced and time-consuming. Therefore, the SIFT algorithm is applied directly to remote sensing. Image processing is not practical, and this algorithm has not been used in real life to deal with remote sensing images. However, the SIFT algorithm has the advantages of other image registration algorithms, and it has a very good application prospect.
To this end, we put forward an improved scheme based on LDA algorithm through a lot of research. That is, adding the linear discriminant analysis (Linear DiscriminantAnalysis, LDA) in the feature extraction of the SIFT algorithm to reduce the dimension of the feature extraction of SIFT. This paper focuses on the feature principle and registration steps of the SIFT algorithm based on LDA. Firstly, the feature point vector of the image is extracted by SIFT algorithm, and then the feature extraction and dimension reduction are carried out by the linear discriminant analysis method. The experimental results show that the LDA-SIFT algorithm is better than the traditional SIFT calculation by using the remote sensing image, the high dimensional natural image and the single face image. The matching time is significantly shorter than that of the traditional SIFT algorithm. This scheme can not only maintain the high accuracy of the SIFT algorithm, but also improve the matching efficiency and the real-time performance. It is very suitable for high dimension image registration, especially for the registration of remote sensing images with higher time and precision.
The main research results of this paper are as follows:
(1) the paper analyzes the principle of the SIFT algorithm and the theoretical basis of the linear discriminant analysis method LDA, and describes the concrete principle of the improved algorithm and the steps of the image registration proposed in this paper. Using the features of the easy realization of LDA, the algorithm is combined with the SIFT algorithm, thus the global optimization is realized and the feasibility of the algorithm is improved.
(2) the improved algorithm is designed by programming and applied to remote sensing image registration, natural image registration and face image registration. Through experiments, it is proved that this algorithm can solve the problem of the poor real-time performance of the traditional algorithm when matching the large data of remote sensing images. At the same time, high matching rate improves the robustness of the matching algorithm.
The improved SIFT algorithm proposed in this paper, that is, LDA-SIFT algorithm can not only reduce the registration time of remote sensing images, but also greatly improves the registration efficiency, which can better meet the demands of people in today's society on the accuracy and efficiency of real-time dynamic data.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 徐海黎;花國然;莊健;王孫安;;采用小世界免疫克隆算子的頻率域圖像配準(zhǔn)[J];西安交通大學(xué)學(xué)報(bào);2009年06期

2 張翼;王滿寧;宋志堅(jiān);;脊柱手術(shù)導(dǎo)航中分步式2D/3D圖像配準(zhǔn)方法[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2007年09期

3 趙書斌;王海川;;圖像配準(zhǔn)及其在目標(biāo)精確定位中的應(yīng)用[J];火力與指揮控制;2007年10期

4 上官晉太;郭慧;楊汝良;祁海明;;圖像配準(zhǔn)的統(tǒng)計(jì)數(shù)值分析[J];測試技術(shù)學(xué)報(bào);2007年06期

5 張學(xué)峰;李麗娟;劉珂;;圖像配準(zhǔn)方法及其在目標(biāo)跟蹤中的應(yīng)用[J];航空兵器;2008年06期

6 李偉;王伯雄;王雪;彭映成;秦W

本文編號(hào)：2043609