MOMO_SS_multipath

说明：  基于模板匹配和肤色检测的人脸检测，包括肤色分割，类似人脸区域检测，模板调整及模板匹配的代码(Template matching and color-based face detection testing, including skin color segmentation, similar to the face region detection, templates and template matching to adjust the code)

说明：  本代码是GAUSS-SEIDEL迭代法、选主元JORDAN消去法、简单GAUSS消去法、选主元GAUSS消去法的MATLAB程序，有详细注释，可直接调用(This code is GAUSS-SEIDEL iteration, pivoting JORDAN elimination, simple GAUSS elimination, pivoting GAUSS elimination of the MATLAB program, with detailed notes, can be called directly)

matlab code for backpropagation

The basic steps of the connected-component text extraction algorithm are given below, and diagrammed in Figure 10. The details are discussed in the following sections. 1. Convert the input image to YUV color space. The luminance(Y) value is used for further processing. The output is a gray image. 2. Convert the gray image to an edge image. 3. Compute the horizontal and vertical projection profiles of candidate text regions using a histogram with an appropriate threshold value. 4. Use geometric properties of text such as width to height ratio of characters to eliminate possible non-text regions. 5. Binarize the edge image enhancing only the text regions against a plain black background. 6. Create the Gap Image (as explained in the next section) using the gap-filling process and use this as a reference to further eliminate non-text regions the output.

This file takes cropped yale face images file as input and performs classifications based on 1. Correlation 2. Eigen vector based classification (Eigen Face) 3. Fisher Linear Discriminator (FLD) (Fisher Face) The zip contains two files readyaledata.m to read the image database EigenFace-FisherFace.m to do classification as described above

基于matlab的简单的人脸识别的代码,希望对大家有用(face detection)

说明：  电网经过整流使用传统DPT控制器的设计与仿真(Design and Simulation of traditional DPT controller for power grid after rectification.)

matlab串口通讯实例,是初学matlab串口的好例子 (matlab examples of serial communications is serial matlab beginner a good example of)

成功实现计算GNqDrh算法的程序，本人测试已经通过，性能已基本达到GNqDrh算法的性能，需要MATLAB 2009b环境运行，如有问题，欢迎讨论 (The successful implementation of the program to calculate GNqDrh algorithm, I have passed the test, need MATLAB2009b operating environment, any questions, please contact me )

费希尔线性判别分析代码 Find the Fisher linear separator w (a column vector). X is is the training set (X is a matrix. Each row of X is a vector containing the features of a single sample). y is a column vector with the labels of the training set (1 and -1). alg is a number between 1 and 3 that specifies how to find w: alg = 1: w is the generalized eigenvalue of A,B. alg = 2: w is the solution to Bw = (mu_1-mu_2) alg = 3: solve the corresponding regression problem( Find the Fisher linear separator w (a column vector). X is is the training set (X is a matrix. Each row of X is a vector containing the features of a single sample). y is a column vector with the labels of the training set (1 and-1). alg is a number between 1 and 3 that specifies how to find w: alg = 1: w is the generalized eigenvalue of A,B. alg = 2: w is the solution to Bw = (mu_1-mu_2) alg = 3: solve the corresponding regression problem)