这是一些基本的matlab的图像处理源码，经过编译通过，完全可以运行，非常好用...

海水的制作 很基础很实用的-water production base is very practical

利用matlab编写的一个中值滤波源程序，希望对大家有用。-Matlab prepared using a median filter source, in the hope that useful to everyone.

图象拼接SIFT算法的C原代码--加州大学一个博士生编的.下面是它的几个不同的版本,挑着用吧!-sciences image stitching algorithm C source code-- the University of California, a doctoral student in the series. Below are several of its different versions carry a use!

本程序基于Matlab,初步实现图像增强的基本操作,实现 图像明暗度调整， 图像旋转，更改颜色显示方式，直接灰度调整，图像求反，动态范围压缩，显示原始图像直方图，直方图均衡化，滤波增强等功能-This procedure based on Matlab, the initial realization of the basic operation of image enhancement, image brightness adjustment, image rotation, change the color display, direct gray-scale adjustment, for anti-image, dynamic range compression, display the original image histogram, histogram equalization , enhanced features such as filtering

该程序是用于图像处理的，包括对位图进行滤波，阈值分析等功能-that the procedure was used for image processing, including bitmap filtering, threshold analysis, and other functions

在VC6.0中的一些基本操作,常常用来进行简单的学习和常规的练习 -in VC6.0 of some of the basic operations, often used for the study and simple conventional exercise

vc++和OpenCV结合找出图片中的角点，并且做出标记。此程序同时可以用于图片特征点的提取，用于特征匹配的后续开发做准备。-vc++ and OpenCV combination to find the picture of the corner and make a tag. At the same time, this procedure can be used for image feature points extraction, feature matching for the follow-up to prepare for development.

介绍了世界范围内在计算机视觉领域的有突出研究成果的实验室极其个人，对介绍了计算机视觉领域的期刊杂志排名情况-Introduced the field of computer vision inherent in the world of laboratory research results highlight the very personal, and introduced the field of computer vision journals ranked

基于数学形态学的模糊边缘检测算法，及相关的开闭运算等-based on mathematical morphology of the fuzzy edge detection algorithms, and related opening and closing, etc.

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.