基于SIFT算法的快速图像配准技术
发布时间：2019-05-28 09:55
【摘要】：近年,图像配准技术广泛应用于建筑学、医学以及军事学等领域。现实应用中,由于获取所需图像的整体面貌比较困难,所以可以先获取局部图像再运用图像配准技术将多幅局部图像拼接成所需图像的整体面貌。图像配准的速度与准确率是目前图像处理领域中的热点与难点。如何提升图像配准的速度和图像拼接的质量在各研究领域中有着重要的意义。图像配准即针对两幅或多幅有共同部分的图像,对它们进行匹配与拼接来得到一张完整图像的技术。实现该技术的方法大致分为三类,分别基于图像的灰度信息、变换域以及图像的特征。本文中的SIFT(Scale-invariant feature transform)算法即是基于图像中的特征进行图像配准的算法。该算法在图像的不变特征提取方面效果很好,但对于某些特定的图像,如:噪声多、像素高及某些信息位置集中的图像,该算法的运算速度会较慢,不能很好的保证高实时性的特定要求,仍然存在不足。本文首先对SIFT算法及其变种算法的原理与过程进行了深入讨论,并进行实验过程对比分析,在此基础上,针对SIFT算法应用于某些特定图像运算速度上的不足进行改进。即运用SVD(Singular value decomposition)方法对初始图像进行压缩处理,通过压缩,选取合适的特征值数量,使得图像中提取的特征点描述子减少,从而减小算法的运算量,达到减少运算时间的目的。通过数值实验结果分析,验证了图像压缩处理过程中使用特征值的数量与算法提取到的特征点描述子的总数量成正比关系,以及特征点描述子的总数量与算法的平均运算时间成正比关系,即本文的改进提升了SIFT算法的运行速度。改进后的算法在保证原有准确率的同时,计算速度比原有算法更快,为图像配准的工作提供了很好的保障。
[Abstract]:In recent years, image registration technology has been widely used in architecture, medicine, military and other fields. In practical application, because it is difficult to obtain the overall appearance of the required image, it is possible to obtain the local image first and then use the image registration technology to mosaic multiple local images into the overall appearance of the required image. The speed and accuracy of image registration is a hot and difficult point in the field of image processing. How to improve the speed of image registration and the quality of image mosaic is of great significance in various research fields. Image registration is the technique of matching and stitching two or more images with common parts to get a complete image. The methods of implementing this technology are divided into three categories, which are based on the gray information of the image, the transform domain and the characteristics of the image. The SIFT (Scale-invariant feature transform) algorithm in this paper is an image registration algorithm based on the features in the image. The algorithm has a good effect on image invariant feature extraction, but for some specific images, such as high noise, high pixel and some information position sets, the operation speed of the algorithm will be slow. There are still shortcomings in the specific requirements that can not guarantee high real-time performance. In this paper, the principle and process of SIFT algorithm and its variant algorithm are discussed in detail, and the experimental process is compared and analyzed. On this basis, the application of SIFT algorithm to some specific image operation speed is improved. That is, the SVD (Singular value decomposition) method is used to compress the initial image, and the appropriate number of eigenvalues is selected to reduce the number of feature points described in the image, thus reducing the computational complexity of the algorithm. To achieve the purpose of reducing operation time. Through the analysis of numerical experimental results, it is verified that the number of eigenvalues used in image compression processing is proportional to the total number of feature point descriptors extracted by the algorithm. And the total number of feature point descriptors is proportional to the average operation time of the algorithm, that is to say, the improvement of this paper improves the running speed of SIFT algorithm. The improved algorithm not only ensures the original accuracy, but also calculates faster than the original algorithm, which provides a good guarantee for image registration.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
[Abstract]:In recent years, image registration technology has been widely used in architecture, medicine, military and other fields. In practical application, because it is difficult to obtain the overall appearance of the required image, it is possible to obtain the local image first and then use the image registration technology to mosaic multiple local images into the overall appearance of the required image. The speed and accuracy of image registration is a hot and difficult point in the field of image processing. How to improve the speed of image registration and the quality of image mosaic is of great significance in various research fields. Image registration is the technique of matching and stitching two or more images with common parts to get a complete image. The methods of implementing this technology are divided into three categories, which are based on the gray information of the image, the transform domain and the characteristics of the image. The SIFT (Scale-invariant feature transform) algorithm in this paper is an image registration algorithm based on the features in the image. The algorithm has a good effect on image invariant feature extraction, but for some specific images, such as high noise, high pixel and some information position sets, the operation speed of the algorithm will be slow. There are still shortcomings in the specific requirements that can not guarantee high real-time performance. In this paper, the principle and process of SIFT algorithm and its variant algorithm are discussed in detail, and the experimental process is compared and analyzed. On this basis, the application of SIFT algorithm to some specific image operation speed is improved. That is, the SVD (Singular value decomposition) method is used to compress the initial image, and the appropriate number of eigenvalues is selected to reduce the number of feature points described in the image, thus reducing the computational complexity of the algorithm. To achieve the purpose of reducing operation time. Through the analysis of numerical experimental results, it is verified that the number of eigenvalues used in image compression processing is proportional to the total number of feature point descriptors extracted by the algorithm. And the total number of feature point descriptors is proportional to the average operation time of the algorithm, that is to say, the improvement of this paper improves the running speed of SIFT algorithm. The improved algorithm not only ensures the original accuracy, but also calculates faster than the original algorithm, which provides a good guarantee for image registration.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
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