本程序将SIFT与边缘结合起来用来做图像配准 先用SIFT检测关键点 用CANNY算子进行边缘检测 进而优化选取最佳匹配点 进行向量匹配 图像配准...

本程序主要实现对退化图像进行复原，主要有运动模糊图像复原，逆滤波复原，维纳滤波复原-The main achievement of this procedure on the degradation of image recovery, there are motion-blurred image restoration, inverse filtering to recover, Wiener filter restoration

TUKE.txe文件中有200个离散点的坐标值,本程序实现生成这些点的凸壳.-TUKE.txe document is 200 discrete points of the coordinate value, The program generating these points of the convex hull.

用PCNN模型实现自动波的反向传播 ************************************* Function PCNN_autowavecp(J) 参数说明：J为输入的二值图像。 功能：用PCNN模型实现自动波的反向传播 -PCNN model with automatic back-propagation wave ************************************* Function PCNN_autowavecp (J) parameters: J for the input binary image. Function: PCNN models with automatic back-propagation wave *************************************

系统介绍了消息传递界面MPI 基础-集群通信 与派生数据类型-Systematically introduce the message passing interface MPI-based- cluster communications and derived data types

混合高斯模型建模，对视频中背景进行混合高斯建模，在摄像机不动的环境中应用广泛-Gaussian Mixture Modeling of video Gaussian mixture background modeling, at the fixed cameras are widely used environment

flash网站中，经常要用到导航条，这里给大家提供一些很精美的实例，相信大家会很喜欢的-flash site, often used in navigation bar, here to provide some very fine examples, I believe everyone will be very like

这是我用所学的知识写得阈值分割程序，希望对大家有一定的启发-This is my knowledge with written procedures for threshold segmentation, in the hope that everyone has a certain degree of inspiration

用matlab仿真图像超分辨率重建技术中的正则化处理方法-Simulation using matlab image super-resolution reconstruction of the regularization approach

方块编码程序 本程序采用了最简单的方块编码对512×512的lena图像进行了编码 运行程序后可以看出，方块取得越大，图像编码后失真越大。 -Box coding procedures used in this process the most simple box of 512 × 512 encoding the lena image after the encoding run the program we can see that the box to obtain the greater the greater the distortion image is encoded.

用计算机进行数字图像处理，使用matlab环境，对图像进行增强，平滑与锐化，分割，变换，压缩与编码，是平常实验的好选择。-Using computer digital image processing, the use of matlab environment, enhance the image, smoothing and sharpening, segmentation, transform, compression and encoding, is usually a good choice experiment.