基于D2层近零点模板的SIFT特征提取算法  

SIFT FEATURE EXTRACTION ALGORITHM BASED ON D2 LAYER NEARLY ZERO TEMPLATE

作　　者：刘影[1] 陆安江[1] 张正平[1] 周钰川[1] 

机构地区：[1]贵州大学计算机科学与信息学院,贵州贵阳550025

出　　处：《计算机应用与软件》2013年第10期173-176,共4页Computer Applications and Software

基　　金：贵州省农业攻关(黔科合NY字[2011]3107号);贵州省社发攻关项目(黔科合SY字[2011]3008);贵阳市科技攻关项目(筑科合同[2011204]34号

摘　　要：SIFT(Scale-Invariant Feature Transform)特征提取是通过在不同尺度DOG(Difference of Gaussian)层进行逐个像素遍历获取极值点得到,当图像分辨率较高时计算量巨大。利用DOG(高斯一阶差分)层极值点与D2(高斯二阶差分)层的近零点之间的对应关系,提出基于近零点模板的SIFT特征提取算法,其中近零点判定阈值利用图像熵动态获取。由于模板的限制致使SIFT特征提取的范围缩小,极大降低了计算及时间复杂度。实验结果表明,相对于经典SIFT算法,提出的算法不仅保持了其较高的鲁棒性,而且大幅提高了特征提取效率。Feature extraction of scale-invariant feature transform （SIFT） is implemented by obtaining the extreme points through one-by-one pixel traversal in different scales layers of difference of Gaussian （DOG）. As the image resolution increases, the calculation becomes enormous. By employing the corresponding relation between the extreme points in DOG layer and the nearly zero points in Gaussian second-order differential （D2） layer, we put forward the SIFT feature extraction algorithm which is based on nearly zero template. In it the nearly zero point discriminant threshold is obtained by making use of the image entropy dynamics. The limitation of the template greatly reduces the scope of SIFT feature extraction, calculation and time complexity. Experiment result shows that in contrast with the classic SIFT, the algorithm proposed in this paper preserves its higher robustness and dramatically improves the feature extraction efficiency as well.

关 键 词：SIFT特征 D2层 近零点模板 图像熵 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]