基于ORB和SLIC超像素分割的特征点匹配方法
Feature Point Matching Method Based on ORB and SLIC Superpixel Segmentation
DOI: 10.12677/CSA.2019.911225, PDF,    科研立项经费支持
作者: 覃 丹*, 刘兴林：江门五邑大学智能制造学部，广东 江门；崔 岩：江门五邑大学智能制造学部，广东 江门;珠海四维时代网络科技有限公司，广东 珠海
关键词: ORB算法；SLIC超像素分割；特征点提取；特征点匹配率；ORB Algorithm； SLIC Superpixel Segmentation； Feature Point Extraction； Feature Point Matching Rate
摘要: 为了增加特征点的提取数量以及提高特征点匹配正确率，本文提出一种基于ORB和SLIC超像素分割的特征点匹配方法。首先利用ORB算法提取出特征点进行匹配，同时加入SLIC超像素分割算法的限制因素进一步提高精度，最后得到优化的特征点匹配结果。本文将SIFT算法，SURF算法和ORB算法相比较，以多次测试求取平均值的方式得出实验数据，得出特征点提取数量的比较情况和特征匹配准确率的比较情况。
Abstract: In order to increase the extracted number of feature points and the matching accuracy of feature points, this paper proposes a feature point matching method based on ORB and SLIC superpixel segmentation. At first, the ORB algorithm was used to extract feature points for matching, and the limiting factors of SLIC superpixel segmentation algorithm were added to further improve the accuracy. Finally, the optimized feature point matching results were obtained. In this paper, SIFT algorithm, SURF algorithm and ORB algorithm were compared. The experimental data were calculated by means of multiple tests to obtain the average value, and the comparison of extraction number of feature points and feature matching accuracy was obtained.
文章引用：覃丹, 崔岩, 刘兴林. 基于ORB和SLIC超像素分割的特征点匹配方法[J]. 计算机科学与应用, 2019, 9(11): 2002-2009. https://doi.org/10.12677/CSA.2019.911225

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