基于梯度角度的直方图局部特征描述子的图像匹配算法  

Image matching algorithm based on histogram of gradient angle local feature descriptor

作　　者：方智文[1,2] 曹治国[1] 朱磊[1] 

机构地区：[1]华中科技大学自动化学院,武汉430074 [2]湖南人文科技学院能源与机电工程系,湖南娄底417000

出　　处：《计算机应用》2015年第4期1079-1083,共5页journal of Computer Applications

基　　金：中国博士后科学基金资助项目(2014M562028);湖南省教育厅项目(14C0599)

摘　　要：针对传统的局部特征描述子在图像匹配效果和效率上很难兼顾的问题,提出了一种基于梯度角度的直方图(HGA)的图像匹配算法。该算法先通过加速片段测试特征(FAST)获取的图像关键点,然后采用块梯度计算和飞镖靶型结构对局部区域的结构特征进行描述。HGA有效地实现了在旋转、模糊、亮度等多种变换下的良好匹配性能,并在一定程度上具备抗仿射变换的能力。在各种复杂场景下,与高速鲁棒描述子(SURF)、尺度不变特征转换(SIFT)和FAST定向的抗旋转二进制鲁棒独立基元特征(BRIEF)描述子(ORB)进行的实验对比表明基于梯度角度的直方图局部特征描述子达到了匹配效果和效率的均衡,算法时间约为SIFT的1/3,点对匹配准确率均在94.5%以上。In order to solve the problem that it is difficult to leverage the performances of effect and efficiency, an image matching algorithm based on the Histogram of Gradient Angle（ HGA） was proposed. After obtaining the key points by Features from Accelerated Segment Test（ FAST）, the block gradient and the new structure as dartboards were introduced to descript the local structure feature. The image matching algorithm based on HGA can work against the rotation, blur and luminance and overcome the affine partly. The experimental results, compared with Speeded Up Robust Feature（ SURF）, Scale Invariant Feature Transform（ SIFT） and ORB（ Oriented FAST and Rotated Binary Robust Independent Elementary Features（ BRIEF））in the complex scenes, demonstrate that the performance of HGA is better than other descriptors. Additionally, HGA achieves an accuracy of over 94. 5% with only 1 /3 of the time consumption of SIFT.

关 键 词：角度直方图 局部特征描述子 多自由度 结构特征 图像匹配 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]