First, the procedure is

以VC2005为工具些的人脸动画的程序代码，对于人脸的变形有很好的参考价值。-VC2005 as a tool to some of the facial animation of the program code, for Facial deformation has a very good reference value.

ROI_PAC著名的遥感图像处理免费软件。可用于InSAR（干涉合成孔径雷达）图像的处理。-ROI_PAC well-known remote sensing image processing free software. Can be used for InSAR (interferometric synthetic aperture radar) image processing.

国外著名大学成功的人脸检测示例中一部分！包含多种环境下的代码-famous University of face detection illustration part! Covering a wide variety of environment code

有从摄像头提取图象,并对其进行相应的处理,包括运动物体的检测,彩色边缘提取!!希望对大家有点用-are extracted from the camera image, and its corresponding treatment, including the detection of moving objects, Color Edge Extraction! ! We hope to use a bit! !

信息隐藏软件Outguess的源码 Outguess主要用于JPEg图象的steganography，本软件来自Outguess的作者Provos，欢迎大家下载研究-Information Hiding Outguess source software is mainly used Outguess JPEG image steganography, this software from Outguess author of Provos, welcomed everyone to download research

手写数字识别，能识别0-9，识别精度高，程序简单-Handwritten numeral recognition, to identify 0-9, identification of high accuracy, simple

这是一个使用域值法编写的图像分割程序，主要是用于学习数字图像处理的同学。-This is a prepared using the threshold method of image segmentation process, mainly for the students to learn digital image processing.

sift算法的c算法，基于opencv的，一个大牛写得，很厉害的。-c algorithms sift algorithm, based on the opencv, a large cattle write, it is powerful.

（1）hough1.m计算所有点的直线参数 ，并将参数存入累加数组中； （2）houghpeaks.m文件进行峰值检测 （3）houghlines.m对图像中的线段检测和链接：-(1) hough1.m straight points for the calculation of all parameters, and parameters into the cumulative array (2) houghpeaks.m document peak detector (3) houghlines.m segment the image in the detection and Links:

These are the codes in "A note on two-dimensional linear discrimant analysis", Pattern Recognition Letter In this paper, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assumption that the same dimensionality is considered. In experimental parts, on one hand, we confirm the validity of our claim and show the matrix-based methods are not always better than vector-based methods in the small sample size problem on the other hand, we compare several distance measures when the feature matrices and feature vectors are adopted.