是关于人脸识别和特征提取的图像处理的c++程序

24位与256位图间的相互转换 转换24位真彩色文件到黑白灰度BMP图片 可提供处学者参考-24 and 256 map conversion conversion between True Color 24-bit grayscale BMP file to black and white picture can provide scholars Reference Department

阈值处理 参数为阈值区间上下限 以及与之内外替代值 -Threshold parameters for the threshold interval and outside the upper and lower limits, as well as with alternative values

基于拓扑导数的图像分割算法，利用某一函数计算图像拓扑导数，并为每个像素归类，从而达到分割效果。-Topology-based derivative of the image segmentation algorithm, using a function derivative calculation of the image topology, and to classify each pixel to achieve the segmentation results.

一个将将bmp文件转换为txt文件的代码，适合初学者-a bmp documents will be converted to txt document code for beginners

用ICA方法分离混合信号，采用H_J方法，输入信号为超高斯信号，如声音信号-Mixed-signal separation using ICA method ―H_J method, the input signal for the super-Gaussian signals, such as acoustic signals

一个利用bcb写成的影像处理程序包含(灰阶.二值化.低通.边缘.侦测旋转.侵蚀.膨胀.透明)提供初学者便于bcb上手- The image disposal procedure which wrote using bcb contains (the gray scale Two values Low pass Edge Detects revolves Corrosion Inflation Transparent) provides the beginner to be advantageous for bcb to begin

Canny边缘检测算法，Vc6.0（sp6）编译运行通过-Canny edge detection algorithm, Vc6.0 (sp6) compiler to run through

与计算机图形学有关，DDA划线法，可以划直线，调用可划多边形-with the computer graphics, DDA crossed, we could draw a straight line, polygon call to be designated

基于小波和形态学的自然地面背景红外目标检测方法-Based on wavelet and morphology of the natural terrestrial background infrared target detection method

（1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 -(1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.