iekfpf_demos

利用脉冲耦合神经网络（pcnn）实现图像滤波的源程序，已经过测试。(Using pulse coupled neural networks (pcnn) the realization of the source image filtering, has been tested.)

图像处理的小波变换领域得到了快速发展与应用，本文分析在遥感图像增强中的应用及MATLAB实现(Imagery processing s wavelet transformation domain obtained the fast development and the application, this article analyzes in the remote sensing image intensification the application and MATLAB realizes )

Kullback-Leibler Importance Estimation Procedure利用Matlab程序实现图像间KL散度的阈值分割(Kullback-Leibler Importance Estimation Procedure procedure using Matlab implementation KL divergence between the image of the threshold segmentation)

说明：  此程序是关于车牌识别重的一个重要环节——图像的二值化过程，希望能给你一些帮助(this program is heavy on the license plate to identify an important part of the-- the two values image process and hope to give you some help)

金字塔模板匹配，一个很有商业价值的软件【我背着导师上传给大家的，希望对整个中国的相关领域开发者有帮助】(Pyramid template matching, a great commercial value of software 【I tutor upload their backs to you, and I hope the related areas of China as a whole help developers】)

利用最小二乘法实现图像变形,提出一种基于控制曲线集的移动最小二乘图像变形算法。根据图像的形状拓扑关系或轮廓信息设置点，生成控制曲线，移动控制曲线生成图像的仿射变换、相似变化、刚性变换。 (The technology of image deformation.Using Moving Least Squares.)

Latent SVM算法实现行人检测opencv(Latent SVM Pedestrian Detection Based on Algorithms opencv)

可以用于图像分割的KNN分类器，简单好用！(knn classification)

说明：  可以读取输出浮点型、整型、复数型影像，对于遥感专业的所有影像读入输出MATLAB中有很大帮助。采用振幅离差阈值提取PS点(Can read floating-point, integer, and complex type of image, for all remote sensing professional images into the MATLAB will be very helpful)

程序设计步骤： 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量(Programming steps: 1 to mean 2, calculation of covariance matrix and its eigenvalues and eigenvectors 3, the calculation of covariance matrix eigenvalues greater than the threshold number of 4, in descending order of eigenvalue 5, remove the smaller eigenvalue 6, remove the larger eigenvalue (usually not this step) 7, the combined choice of eigenvalues 8, select the appropriate eigenvalues and eigenvectors 9, computing whitening matrix 10, principal component extraction)