基于爬山搜索的高斯模糊不变SIFT算子  

GAUSSIAN BLUR INVARIANT SIFT OPERATOR BASED ON HILL-CLIMBING SEARCHING

作　　者：付波[1] 张敏[1] 赵熙临[1] 李超顺[2] Fu Bo;Zhang Min;Zhao Xilin;Li Chaoshun(School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, Hubei, China;School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 , Hubei, China)

机构地区：[1]湖北工业大学电气与电子工程学院,湖北武汉430068 [2]华中科技大学水电与数字化工程学院,湖北武汉430074

出　　处：《计算机应用与软件》2016年第6期185-189,共5页Computer Applications and Software

基　　金：国家自然科学基金项目(61072130;51109088);武汉市科技攻关计划项目(2013012401010845);湖北工业大学科研基金项目(BSQD12107);广东省工业攻关项目(2011B010100037)

摘　　要：针对SIFT算子对于高斯模糊环境下的特征匹配困难,提出基于目标图像形变空间重采样的高斯模糊不变SIFT算子GISIFT(Gaussian Invariant SIFT)。首先构建清晰目标的高斯模糊模型,重采样模型参数重建目标图像完备形变空间;其次,引入降采样与爬山法,构建目标图像的降采样形变空间,在降采样空间中以大采样步长快速搜索当前峰值,对峰值邻域进行曲线拟合,快速找到最优匹配。实验结果表明,所提算法不仅对高斯模糊目标能较好匹配,同时较大提升了目标的特征匹配效率。SIFT operator is difficult in feature matching in Gaussian blur environment.Aiming at this problem,we proposed a Gaussian blur invariant SIFT operator (GI-SIFT)which is based on resampling in deformation space of object image.First,we built the Gaussian blur model of the clear object and re-sampled the model parameters to reconstruct complete deformation space of the object image.Secondly,we introduced subsampling and hill climbing approaches to construct the subsampling deformation space of the object image,and rapidly searched the current peak value in subsampling space with large sampling step,and made curve fitting in peak neighbourhood to quickly find the optimal matching.Experimental results showed that the proposed algorithm can well match Gaussian blur object,at the same time it also greatly improves the efficiency of objects feature matching.

关 键 词：度不变特征变换 形变空间重采样 高斯模糊 降采样 爬山法 特征匹配 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]