基于改进SURF算法图像匹配方法研究

发布时间：2018-06-29 22:38

本文选题：图像匹配 + SURF　；参考：《安徽理工大学》2017年硕士论文

【摘要】：图像匹配作为图像处理技术的一大分支,近年来在图像处理领域占据着重要的地位。许多计算机视觉方面的研究都是在假设匹配问题已经得到解决过后去展开工作的。针对于图像匹配而言,现今的图像匹配算法包括两类:一类是基于灰度的匹配,一类是基于特征点的匹配。本文在图像匹配方面的研究都是根据特征点匹配下情况下展开的,对一些经典的基于特征点图像匹配算法作了详细的研究,然后针对SURF算法进行了改进。首先,本文总结了国内外图像匹配方面的研究与发展,并对现下流行的基于特征点的图像匹配算法分析了国内外的研究现状、常见的研究方法等。其次,重点就一些经典的基于特征点的匹配算法的理论原理进行了介绍,主要包括特征点的提取,特征描述符的建立和匹配方法等。同时依赖于计算机视觉开源库OpenCV开发,通过具体的实验来对各种匹配算法性能进行对比,分析实验数据,评估实验结果。最后,依赖于开源视觉库OpenCV开发,采用算法oFAST检测特征点与SURF算法建立描述符相结合,同时在匹配方法中,结合使用LBPH演化算法,来逐步提高匹配精度。在对改进的SURF算法实验评估中发现,改进的算法虽然在匹配效率上有所降低,但在匹配精度上有了明显的提高。
[Abstract]:As a branch of image processing technology, image matching plays an important role in the field of image processing in recent years. Many computer vision studies work on the assumption that the matching problem has been solved. As far as image matching is concerned, there are two kinds of image matching algorithms: one is based on gray level, the other is based on feature points. In this paper, the research on image matching is carried out under the condition of feature point matching. Some classical feature point based image matching algorithms are studied in detail, and then the SURF algorithm is improved. Firstly, this paper summarizes the research and development of image matching at home and abroad, and analyzes the current research situation and common research methods of image matching algorithms based on feature points. Secondly, some classical matching algorithms based on feature points are introduced, including feature point extraction, feature descriptor establishment and matching methods. At the same time, it relies on OpenCV, an open-source computer vision library, to compare the performance of various matching algorithms, analyze the experimental data and evaluate the experimental results through specific experiments. Finally, based on the OpenCV development of open source vision library, the algorithm oFAST is used to detect feature points and SURF algorithm is used to establish descriptors. In the matching method, LBPH evolution algorithm is combined to improve the matching accuracy step by step. In the experiment evaluation of the improved surf algorithm, it is found that although the improved algorithm has lower matching efficiency, the accuracy of the improved algorithm has been improved obviously.
【学位授予单位】：安徽理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41

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