鲁棒的新型特征提取和匹配算法  

Robust novel feature extraction and matching algorithms

作　　者：王海罗[1] 汪渤[1] 

机构地区：[1]北京理工大学自动化学院,北京100081

出　　处：《吉林大学学报（工学版）》2013年第S1期371-375,共5页Journal of Jilin University:Engineering and Technology Edition

摘　　要：针对现有图像特征匹配算法高复杂度、耗时长等问题,提出一种基于局部特征点的新型特征匹配算法。首先,构建尺度金字塔,在不同的尺度上进行FAST特征点检测,根据特征点的Harris响应对特征点进行排序选取;然后利用图像的矩和积分图的方法获得特征点方向,再根据同心圆的采样模式构造特征点向量,最后根据特征点向量的汉明距离进行特征匹配。实验研究表明,该算法在图像有一定程度的缩放、旋转和噪声影响的条件下,运行效果仍然稳定可靠。与传统的SIFT算法相比,该算法在保证特征提取与匹配良好性能的前提下,运行速度要比SIFT算法快数倍。Now existing image feature matching algorithms are always high complexity and long time-consuming.A novel feature matching algorithm was proposed based on local feature points.Scale pyramid should be constructed first in which FAST key points were detected and extracted according to their Harris response.Then directions were distributed for key points using a method of intensity centroid.Finally,key point vectors were built via a sampling pattern.The hamming distance between the key point vectors in different images decided whether the two of them were matched or not.Experiments show that this algorithm is robust and reliable even under the condition of a certain degree of scaling,rotation and the effects of noise.Moreover,this algorithm is several times faster than SIFT while performing as well as SIFT in other aspects.

关 键 词：特征提取 特征匹配 尺度金字塔 SIFT算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]