daopujisuan

这是一个比较完整的KPCA算法，通过这个算法可以快速的找到主要的特征向量进行主成分分析。(This is a relatively complete KPCA algorithm, this algorithm can quickly find the main feature vector principal component analysis.)

几种全参考图像质量评价标准，例如VIF、IFC、VSNR、PSNR、MSE等。(Several full reference image quality assessment standard, such as 2 VIF is based, the IFC, VSNR, PSNR, MSE and so on. )

说明：  利用匹配滤波器实现图形识别，包括程序和说明文档(Graphic Recognition Using Matched Filter)

说明：  对于双基地SAR，基于IDL语言使用BP算法进行成像，以及显示回波信号。(For Bistatic SAR, BP algorithm is used to image and display echo signal based on IDL language.)

imageReadAndShow2.cpp,读取asc文件,并在VTK中渲染显示...(read ASC File,using VTK)

一个基于帧间差分的目标跟踪matlab源代码，解压后可直接运行，对学习跟踪有重大帮助(Frame difference of a target tracking based on matlab source code, extract can be directly run a major help in learning track)

说明：  灰度图像的边界跟踪的方法，先用微分算子计算出图像的梯度图。然后再应用此算法。(Gray-scale image of the border tracking methods, first calculate the differential operator of the gradient map images. And then apply this algorithm.)

图片的旋转、截图、灰度、翻转、滤波、去噪等基本处理(Image rotation, screenshots, gray, flip, filtering, noise and other basic processing)

图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。 其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？ 用BoW描述完图像之后，指的是将训练集以及测试集的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。 在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。(Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set. In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem? After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified. Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram )

用arnold变换对lena图进行图像置乱(scramble a picture by arnold transformation)