The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SI...The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SIFT) is proposed,but its computational complexity and complication seriously affect the efficiency of the algorithm.In order to solve this problem,SIFT algorithm is proposed based on principal component analysis(PCA) dimensionality reduction.The algorithm first uses PCA algorithm,which has the function of screening feature points,to filter the feature points extracted in advance by the SIFT algorithm;then the high-dimensional data is projected into the low-dimensional space to remove the redundant feature points,thereby changing the way of generating feature descriptors and finally achieving the effect of dimensionality reduction.In this paper,through experiments on the public ORL face database,the dimension of SIFT is reduced to 20 dimensions,which improves the efficiency of face extraction;the comparison of several experimental results is completed and analyzed to verify the superiority of the improved algorithm.展开更多
基金Supported by the National Natural Science Foundation of China (No.61571222)the Natural Science Research Program of Higher Education Jiangsu Province (No.19KJD520005)+1 种基金Qing Lan Project of Jiangsu Province (Su Teacher’s Letter 2021 No.11)Jiangsu Graduate Scientific Research Innovation Program (No.KYCX21_1944)。
文摘The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SIFT) is proposed,but its computational complexity and complication seriously affect the efficiency of the algorithm.In order to solve this problem,SIFT algorithm is proposed based on principal component analysis(PCA) dimensionality reduction.The algorithm first uses PCA algorithm,which has the function of screening feature points,to filter the feature points extracted in advance by the SIFT algorithm;then the high-dimensional data is projected into the low-dimensional space to remove the redundant feature points,thereby changing the way of generating feature descriptors and finally achieving the effect of dimensionality reduction.In this paper,through experiments on the public ORL face database,the dimension of SIFT is reduced to 20 dimensions,which improves the efficiency of face extraction;the comparison of several experimental results is completed and analyzed to verify the superiority of the improved algorithm.
文摘研究眼底图像的精确配准问题,眼底图像配准,需经仿射变换等。由于分辨率不高,达不到要求。针对传统基于眼底图像的血管分支和交叉点等配准方法的局限性,提出一种基于不变特征的眼底图像配准方法。在尺度不变特征变换(ScaleInvariant Feature Transform,SIFT)的基础上,建立特征点对之间的初始匹配,并利用特征点的方向特征和空间几何特性去除误匹配。根据匹配特征进行层次估计,求解图像对间的变换关系矩阵,利用Akaike Information Criteria(AIC)模型选择技术判断变换关系矩阵的类型,再通过得到的变换关系对配准图像进行修正。实验结果表明,改进方法具有良好的配准效果,配准精度可以达到亚像素级要求。
