mrf

一个外国人写的基于分割的立体匹配算法代码，可以在matlab7.1以后版本中直接执行，代码结构也很清晰，适合分析学习(Written by a foreigner based on the partition of the three-dimensional matching algorithm code, you can later matlab7.1 direct the implementation of the code structure is very clear and suitable for analysis of learning)

说明：  图像增强的目标是改进图片的质量，例如增加对比度，去掉模糊和噪声，修正几何畸变等；图像复原是在假定已知模糊或噪声的模型时，试图估计原图像的一种技术。 图像增强按所用方法可分成频率域法和空间域法。前者把图像看成一种二维信号，对其进行基于二维傅里叶变换的信号增强。采用低通滤波（即只让低频信号通过）法，可去掉图中的噪声；采用高通滤波法，则可增强边缘等高频信号，使模糊的图片变得清晰。具有代表性的空间域算法有局部求平均值法和中值滤波（取局部邻域中的中间像素值）法等，它们可用于去除或减弱噪声。 (A sharp and clear image is not directly produced from the optics, however, digital signal processing applied to the sampled image produces a sharp and clear final image that is also insensitive to misfocus related aberrations. This paper gives an overview of Wavefront Coding and example images related to the two applications of machine vision/label reading and biometric imaging. Design techniques of Wavefront Coding are unique from that of traditional imaging system design since both the optics and digital processing characteristics of the system are jointly optimized for optimum system performance.)

连通域处理的函数简介，包含删除小面积对象，计算对象面积，删除边界对象等(Function of connected domain processing)

用VC++实现给定位图的镜像，使用图像处理技术(with VC plans to mirror positioning, the use of image processing technology)

运动目标检测一系列文章，包含很多新的算法，可以参考研究一下(Moving target detection range of articles, includes many new algorithms, you can refer to study)

小波去噪算法，包含多种小波去噪去噪，强制去噪、软阈值去噪、硬阈值去噪(Wavelet denoising algorithm that contains a variety of wavelet denoising denoising forced denoising, soft thresholding and hard threshold denoising )

图像处理中的图像分割grabcut算法，经试验可以达到很高的精确度，比较难，值得研究(Image Processing Image Segmentation grabcut algorithm, the test may be achieving a high degree of accuracy, more difficult, is worth studying)

说明：  灰度图像二值化及目标提取和质心求取,Matlab代码.对初学者学习有用(Gray image binarization and target extraction and center of mass strike, Matlab code. Learn useful for beginners)

2.编写两类正态分布模式的贝叶斯分类程序。 设以下模式类别具有正态概率密度函数： ω1：{(0 0)T, (2 0)T, (2 2)T, (0 2)T} ω2：{(4 4)T, (6 4)T, (6 6)T, (4 6)T} （1）设P(ω1)= P(ω2)=1/2，求这两类模式之间的贝叶斯判别界面的方程式。 （2）绘出判别界面。 3.已知服从正态分布的两类训练样本集分别为 ：，，，， ：，，， ，试问属于哪一类？ 4．设有两类一维模式，每一类都是正态分布，两类的均值和均方差分别为，；，。 采用（0-1）损失函数，且。 （1）试绘出两类模式的密度函数曲线，其判别界面位于何处？ （2）若已获得样本：-3，-2，1，3，5，试判断它们各属于哪一类。(bayes ，matlab)

实现峰值信噪比的算法，用于判断图片质量的好坏！十分有用！(Achieve peak signal-to-noise ratio (PSNR) algorithm for judging image quality! Very useful!)