改进FAST特征点支持下的实时影像地标匹配算法

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。

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

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.