Fourier-Descriptor

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 )

SVM支持向量机在图像分割中的应用，包含Matlab源代码和SVM所用函数。(SVM support vector machine applications, including image segmentation Matlab source code and functions used by SVM.)

使用全变分模型对受噪声污染的图像进行修复(Using the total variation model of the image affected by noise pollution, to repair)

说明：  简单的数字图像处理程序，实现灰度化，二值化，边缘检测等(Simple digital image processing program, graying, binarization, edge detection, etc....)

说明：  掌纹roi提取 掌纹图像处理 图像分割(Extraction of palmprint roi)

labview图像预处理，子程序,包括转化为灰度图，二值化，腐蚀，膨胀，参数可调(labview IMAQ image pretreatment,sub vi，consist of conversion to grayscale, binary, erosion, dilation, adjustable parameters)

此算法为基于k-means聚类的非局部均值快速去噪算法，方法便捷有效，可保持边缘信息(this is a fast edge-preserving denoising method based on non-local means method with k-means clustering)

基于VC++的RGB-IHS 变换实现过程和主成分分析 PCA 实现过程 .(The implementation process based on VC++ RGB-the IHS transform and principal component analysis PCA implementation process.)

该程序是数字图像处理中一个常用的算法，研究在VC6.0下调试通过，并且达到了非常好的效果，希望对大家能有点帮助。(The program is a digital image processing algorithms commonly used to study under VC6.0 debugger through and achieved very good results, in the hope that we can to some extent.)

自己用水平集方法编的，实现了医学图像的分割，效果不错，可试一下(Their allocation by level set method, and realize the medical image segmentation, the effect good, can try)