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说明：  图像配准，控制点配准，边缘检测，。。。。。。。。。。。。。。。(image registration, edge detect, hough line detect,)

刚体配准方法中，通常使用互信息函数作为相似性测度，此类提供计算的封装。(rigid registration method, which is commonly used as a mutual information function similar measure, the provision of such calculation package.)

均值、中值、维纳、TV、小波五种图像去噪方法（matlab）.均经过运行调试。包括对五种方法的研究报告（word）。 (Mean, median, Wiener, TV, xiaobo five kinds of Wavelet Image Denoising (Matlab). Have been run debug. including The study of five ways (word).)

视觉引导车路识别：灰度AGV路径识别；彩色AGV路径识别；HSI彩色空间的AGV路径识别；路径中心线的定位；Radon变换的AGV路径偏差检测(Vision Guided Vehicle Road Identification: gray AGV path identification color AGV path identification HSI color space of the AGV path identification path centerline positioning Radon transform of the AGV path deviation Detected)

这是基于动态规划的立体匹配的代码，是当前立体匹配中很好的一种实现方式(This is the stereo matching based on dynamic programming code that is currently in a good stereo matching method to implement)

迭代CT重建血管，希望对大家能够有所帮助(Iterative CT reconstruction of blood vessels, we want to be able to help)

说明：  用matlab编写的图像复原源代码，采用维纳滤波复原技术，可对模糊和加噪的图像实现精确复原。(Prepared using matlab source code image restoration using Wiener filter restoration technology, can be vague and processors realize accurate image noise resilience.)

SURF Feature is an algorithm that is used in the object recognition please see the source code

对一个图像进行红蓝立体画制作，使得呈现3D效果。(to make a picture viewed as 3D)

一种基于半监督的svm的图像分类方法。该方法通过聚类核的方法利用无标记样本局部正则化训练核的表达式。这种方法通过图像直接学习一个自适应的核。该程序仿真的是文章：Semi-supervised Remote Sensing Image Classification with Cluster Kernels。大家可以参考下。(A semi-supervised SVM is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image, and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictionsds)