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说明：  采用matlab实现高斯金字塔，函数Gscale(img,levels,gsize,sigma)(To achieve Gaussian pyramid using matlab)

本程序集合了互信息配准，PV差值，powell算法，基于小波变换的图像融合的一整套算法。只需要在GUI界面输入两幅图像，即可自动完成图像的配准融合。程序包含Matlab源代码。用Matlab打开imagefusion.m，即会弹出gui界面。融合前要输入参考和浮动图像。图像要求像素的 长＊宽 一样。否则会报错。程序只能处理灰度图像，彩色图像也会转化成灰度图像处理。程序代码已经有详细的注释，方便读者理解。(The procedures set of mutual information, PV difference, powell algorithm, based on wavelet transform image fusion set of algorithms. Only need to enter the two images in the GUI interface, you can automatically complete the image registration fusion. Program contains Matlab source code. Using Matlab open imagefusion.m, gui interface will pop up. Fusion before the input reference and floating images. Image requires the same pixel length* width. Otherwise incorrect. Can only handle grayscale images, color images into gray image processing also. Program code has detailed notes, to facilitate understanding.)

机器视觉开发平台

初步的代码，只保存边界的声波逆时偏移 RTM

程序可以实现BMP图片特效处理,包括浮雕效果(bmp picture)

关联下载,用于灰度相似度说明,欢迎下载使用,我感觉最好开源比较好(Download link for the gray-scale similarity shows that the use of welcome to download, I feel the best open source is better)

susan角点检测算法的MATLAB程序实现(susan corner detection algorithm for implementation of the MATLAB program)

本人收集的BM3D资料，包括文献，代码，实验效果图，希望对大家有用(I collected BM3D information, including documentation, code, experimental results map, want to be useful)

介绍ransac的matlab程序，用在图形图像基于特征点的配准中 可以有效的剔除错误的匹配点 (介绍ransac的matlab程序，用在图形图像基于特征点的配准中 可以有效的剔除错误的匹配点 )

斯坦福大学的姚邦鹏开发的一个图像分类算法，使用random forest实现了图像的精细区域描述，赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试，程序完全可运行，且提供了MAC和Windows下的两种程序。(Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our method is to identify the discriminative fine-grained image region that distinguishes different classes. To achieve this goal we sample image regions from dense sampling space and use a random forest algorithm with discriminative classifier. Each node of the tree of random forest is trained and tested with fine-grained image patches combining the information from upstream nodes together. We implemented each node of the tree with a discriminative SVM classifier, which makes the node as a strong classifier. )