基于网格运动统计的改进快速鲁棒特征图像匹配算法  

Image Matching Algorithm Combining Improved SURF Algorithm with Grid-Based Motion Statistics

作　　者：王晓华[1] 方琪 王文杰 WANG Xiaohua;FANG Qi;WANG Wenjie(School of Electronics and Information,Xi′an Polytechnic University,Xi′an 710048)

机构地区：[1]西安工程大学电子信息学院

出　　处：《模式识别与人工智能》2019年第12期1133-1140,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.51905405);教育部工程科技人才培养研究项目(No.18JDGC029);陕西省自然科学基础研究计划(No.2019JQ-855);陕西省教育厅自然科学专项(No.19JK0375)资助~~

摘　　要：针对快速鲁棒特征算法(SURF)局部不变特征描述符存在运算时间较长、匹配准确率较低的问题,文中提出基于网格运动统计的改进快速鲁棒特征图像匹配算法.首先运用Hessian矩阵行列式确定图像中的特征点,采用梯度方向改进SURF中的主方向提取方法,提高特征点主方向的准确性,并使用二进制特征描述子进行特征点描述.再对获取的特征点进行汉明距离粗匹配.最后,采用网格运动统计剔除误匹配点.在Oxford VGG标准数据集上的实验表明,文中算法在图像发生尺度、光照、旋转等变化时匹配准确率与效率较高.To solve the problems of long operation time and low matching accuracy in the local invariant feature descriptor of speeded up robust features(SURF)algorithm,a image matching algorithm combining improved SURF algorithm with grid-based motion statistics(GMS)is proposed.Firstly,determinant of Hessian is utilized to determine the feature points of the image,and the main direction extraction method in SURF algorithm is improved by gradient direction to increase the accuracy of the main direction of the feature points.The binary feature descriptor rotation-aware binary robust independent elementary feature(rBRIEF)is employed to describe the feature points.Then,the feature points are roughly matched by Hamming distance.Finally,GMS is adopted to eliminate the mismatches.Experiment on Oxford VGG standard dataset indicates that the proposed algorithm achieves higher matching accuracy and efficiency with image changes in scale,illumination and rotation.

关 键 词：特征匹配 特征提取 梯度方向 网格运动统计(GMS) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]