改进FAST特征点支持下的实时影像地标匹配算法

doi:10.11772/j.issn.1001-9081.2016.05.1404

计算机应用 ›› 2016, Vol. 36 ›› Issue (5): 1404-1409.DOI: 10.11772/j.issn.1001-9081.2016.05.1404

改进FAST特征点支持下的实时影像地标匹配算法

杨琪莉, 朱兰艳, 李海涛

昆明理工大学国土资源工程学院, 昆明 650093

收稿日期:2015-09-25 修回日期:2015-11-27 发布日期:2016-05-09 出版日期:2016-05-10
通讯作者: 杨琪莉
作者简介:杨琪莉(1993-),女,云南大理人,硕士研究生,主要研究方向:图像处理、模式识别;朱兰艳(1966-),女,四川西昌人,副教授,硕士,主要研究方向:空间数据处理及误差分析、地理信息系统的质量评估;李海涛(1991-),男,云南楚雄人,硕士研究生,主要研究方向:地理信息系统空间分析、图像处理。

Real-time landmark matching algorithm supported by improved FAST feature point

YANG Qili, ZHU Lanyan, LI Haitao

Institute of Land Resources Engineering, Kunming University of Science and Technology, Kunming Yunnan 650093, China

Received:2015-09-25 Revised:2015-11-27 Online:2016-05-09 Published:2016-05-10

摘要/Abstract

摘要： 针对图像匹配技术中匹配时间与匹配精度不能同时满足要求的问题,提出一种基于特征点匹配的方法,利用随机森林分类器实现地标的匹配,将匹配问题转化为简单的分类问题,大大简化了计算过程,保证影像匹配实时性;采用FAST特征点表示影像地标,利用高斯金字塔结构以及仿射增强策略改进FAST特征点的尺度和仿射不变性,提升影像地标匹配率。将实验结果与尺度不变特征变换(SIFT)算法和加速鲁棒性(SURF)算法进行比较。实验结果表明在尺度变化、发生遮挡以及旋转情况下,匹配率能达到90%左右,保持与SIFT算法和SURF算法相近的匹配率,并且匹配时间相较其他两种算法减少了一个数量级,能有效地对影像地标进行匹配,匹配时间也满足实时影像地标匹配要求。

关键词: 随机森林, 地标匹配, FAST特征点, 高斯金字塔结构, 仿射增强策略

Abstract: Concerning the problem that matching time and accuracy requirements can not be met the simultaneously in image matching technology, a method based on feature points matching was proposed. Landmark matching was achieved successfully by using Random Forest (RF), and matching problem was translated into simple classifying problem to reduce the complication of computation for real-time image matching. Landmark image was represented by Features from Accelerated Segment Test (FAST) feature points, the scale and affine invariability of FAST feature points were improved by Gaussian pyramid structure and affine augmented strategy, and the matching rate was raised. Comparing with Scale-Invariant Feature Transform (SIFT) algorithm and Speed Up Robust Feature (SURF) algorithm, the experimental results show that the matching rate of the proposed algrorithm reached about 90%, keeping the matching rate approximately with SIFT and SURF in cases of scale change, occlusion or rotation, and its running time was an order of magnitude than other two algorithms. This method matches landmarks efficiently and its running time meets the real-time requirements.

Key words: Random Forest (RF), landmark matching, Features from Accelerated Segment Test (FAST) feature point, Gaussian pyramid structure, affine augmented strategy

中图分类号:

TP391.41

杨琪莉, 朱兰艳, 李海涛. 改进FAST特征点支持下的实时影像地标匹配算法[J]. 计算机应用, 2016, 36(5): 1404-1409.

YANG Qili, ZHU Lanyan, LI Haitao. Real-time landmark matching algorithm supported by improved FAST feature point[J]. Journal of Computer Applications, 2016, 36(5): 1404-1409.

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改进FAST特征点支持下的实时影像地标匹配算法

Real-time landmark matching algorithm supported by improved FAST feature point

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