phasecongruency2

分段自适应正交匹配追踪StOMP算法源程序，压缩感知里面很好的算法(Orthogonal Matching Pursuit StOMP adaptive segmentation algorithm source code, a good algorithm inside compressed sensing)

color image watermarking with color

两次利用大津法对图像进行分割，将前景与背景，白色区与背景分割开，并二值化。(Otsu method twice using image segmentation, the foreground and background, white background area and separate, and binary.)

esprit算法对有干扰的信号频率进行估计,一种基于多文档得图像合并技术，搭建OFDM通信系统的框架。( esprit algorithm signal frequency interference can be assessed Based on multi-document image obtained combining technique, Build a framework OFDM communication system.)

调用 envi5.3自带的FMask云检测功能，并输出掩膜文件(Call the FMash cloud detection function of envi5.3 and output the mask file)

计算图像的几何不变矩—Hu矩，以及图像的相似度，在vc下实现。(Calculate the geometric image invariant moments-Hu moments, as well as the similarity of the image, achieved under vc.)

InSAR相位解缠用，好多种方法，参考或者改进其中的算法用(InSAR phase unwrapping, and a good variety of methods, reference, or to improve the algorithm by which)

数字图像处理，分水岭算法matlab源码,可以实现图像分割。(Digital image processing, watershed algorithm matlab source code, can achieve image segmentation.)

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 )

this codes describe several kind of image filters