不同阈值降噪后的效果

运用相位相关法对图像进行匹配，并通过图形形象地显示相关系数位置。对图像的亮度变换不敏感，相关峰尖突出，有较高的配准精度。(fft、ifft、phase correlation)

EKF2.0 利用扩展卡尔曼滤波器实现的SLAM算法，Matlab版本，是一个模拟的程序。运行方法为先载入mat，再运行XXX_sim函数。(EKF2.0 using the extended Kalman filter SLAM algorithm, Matlab version, is a simulation program. The method of operation for the first load the mat, run XXX_sim function.)

说明：  联合特征选择和子空间相关性学习的跨模态检索，map优于典型相关性分析的跨模态检索(Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval)

matlab回归法做图像变化检测。有图片有结果，有论文说明，可参照使用(Matlab regression method for image change detection. There are pictures of the results, there are papers, can refer to use)

说明：  很有用处的一款抑制端点效应的程序，欢迎大家下载试用(A useful program to suppress the end effect, you are welcome to download the trial.)

对场景分类和语义特征稀疏化的高层图像表示(Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification)

在桌面上显示图像，C++Builder精选编程学习源码，很好的学习资料。(Image display on the desktop, C++Builder select learning programming source code, very good learning materials.)

基于K-means的优化聚类中心的图像分割算法，改进了FCM算法的缺点(Based on the optimization of K-means clustering center image segmentation algorithm to improve the shortcomings of the FCM algorithm )

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.)

matlab程序，找一幅图像的几何重心和灰度重心以及一个典型滤波器(Find focus)