这是在图形图像处理中的经典之作，它为一些图片。

边缘就是指图像局部亮度变化最显著的部分，它是检测图像局部变化显著变化的最基本的运算。对于数字图像，图像灰度灰度值的显著变化可以用梯度来表示，以边缘检测Sobel算子为例来讲述数字图像处理中边缘检测的实现-edge means partial image brightness changes the most significant part of it is partial image change detection significantly change the basic arithmetic. For digital images, image intensity values of gray significant gradient changes can be expressed as Sobel edge detection to operator as an example to talk about digital image processing to achieve Edge Detection

用改进的GAC变分水平集方法实现图像分割，简化和避免了重新初始化问题-GAC improved variational level set method to achieve image segmentation, simplification and to avoid the issue of re-initialization

实现亚像素的运动位移估计算法实现，通过双线性内插实现！-Campaign to achieve sub-pixel displacement estimation algorithm to achieve, through bilinear interpolation to achieve!

本程序用VC实现Harris角点检测的算法，浅显易懂，便于操作。-This procedure VC realize with Harris corner detection algorithm, easy to understand and easy to operate.

数字图象处理的各种几何变换的C语言原代码-Digital image processing of a variety of geometric transformations of C language source code

基于隐马尔可夫模型的人脸识别 需要opencv3.1版本才能使用-Based on Hidden Markov Model Face Recognition opencv3.1 versions need to use

一个用labview编写的基于一维最大熵的图像阈值分割方法。使用方法请看Readme.txt-use a PC to prepare a peacekeeping based on maximum entropy image thresholding methods. Look at the use Readme.txt

这是在荷兰埃因霍温科技大学peater教授指导下做得图形识别寻找，在一堆图形中寻找圆形，多边形等图像，理论较为简单，适合与刚开始接触Mathematic的同志们。-This is the Eindhoven University of Technology in the Netherlands under the guidance of Professor peater done looking for pattern recognition, in a pile of graphics look round, polygon, such as images, a relatively simple theory for the beginning of contact with the Mathematic comrades.

图形变换和消隐，matlab guide编写-graphics transformation and blanking, Matlab guide prepared

一系列展示图像压缩技术的源代码。包括有：使用块截断编码的图像压缩(Block Truncation)、基于高斯金字塔变换的图像压缩(Gaussian Pyramids)、基于离散余弦变换对图像压缩(Discrete Cosine Transform)、基于奇异值分解(SVD)的图像压缩(Singular Value Decomposition)。给出的代码还可以用于2D图像噪声消除-A series of image compression technology to display the source code. Include: the use of block truncation coding for image compression (Block Truncation), based on the Gaussian pyramid transform for image compression (Gaussian Pyramids), based on the discrete cosine transform for image compression (Discrete Cosine Transform), based on singular value decomposition (SVD) Image compression (Singular Value Decomposition). Given code can also be used in 2D image noise cancellation