基于分块FLD的图像特征提取方法

提出了基于分块Fisher线性鉴别（Fisher＇s Linear Discriminant,FED）的特征提取方法,先对图像矩阵进行分块,将分块得到的子图像矩阵直接用来构造类内和类间离散度矩阵,然后利用Fisher鉴别函数取极大值时得到的最优投影方向进行图像的特征提取。分块FLD方法是二维FLD方法的推广,该方法可以提取每一单元块的局部特征,在ORL人脸库上的实验结果表明该方法在人脸识别性能方面优于二维FLD方法。     

Point pattern matching based on kernel partial least squares

Point pattern matching is an essential step in many image processing applications.This letter investigates the spectral approaches of point pattern matching,and presents a spectral feature matching algorithm based on kernel partial least squares(KPLS).Given the feature points of two images,we define position similarity matrices for the reference and sensed images,and extract the pattern vectors from the matrices using KPLS,which indicate the geometric distribution and the inner relationships of the feature points.Feature points matching are done using the bipartite graph matching method.Experiments conducted on both synthetic and real-world data demonstrate the robustness and invariance of the algorithm.