Brain-MRI-Ssegmentation

FDK重建算法 希望对其他人能有帮助 用C和cuda 实现的(fdk reconstrcution algorithm using cuda and c source code hope to help others it may be usefull. it is implented by c and cuda)

张正友相机标定源码，数据及图片，包含说有文件，能正常运行(zhang s calibration)

基于DCT域音频水印完整源代码，包括水印图像的预处理（二维二值图像Arnold变换、混沌加密、降维），水印的嵌入提取以及检测，信噪比和相似性系数。(Based on the the DCT domain audio watermarking complete source code, including the watermark image preprocessing (two-dimensional binary images Arnold transform chaotic encryption, dimensionality reduction), the embedded watermark extraction and detection, signal-to-noise ratio and similarity coefficient.)

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 )

基于偏微分方程的遥感图像处理方法 利用测试图像对算法进行仿真验证，并与其它图像去噪算法进行去噪效果比较，通过数值对降噪效果进行评估，得出该方法不仅能够去除噪声而且能够很好的保护图像的边缘等细节信息，弥补了高阶非线性扩散去噪方法导致边界模糊的缺陷，提高了遥感图像的质量，改善了视觉效果(Simulation algorithm of remote sensing image processing methods based on partial differential equations using the test image denoising results compare with other image denoising algorithm to assess the value of the noise reduction effect, draw the method can not only remove the noise but well protected image edge details, to make up for the higher order nonlinear diffusion denoising method lead to fuzzy boundaries defects and improve the quality of remote sensing images and improve the visual effects)

凸一维总变差去噪与非凸正则化，内有文献参考。值得学习(One-dimensional convex total variation denoising non-convex regularization, the literature reference. Worth learning)

C#实现了数字图像处理中的几个边缘检测的算法源代码。(C# implementation of digital image processing edge detection algorithm source code.)

高斯有色噪声，有色噪声的谱估计及其在雷达中的应用(gauss noise)

针对高分辨率遥感图像进行的道路提取，效果还行。(The road extraction for high resolution remote sensing images is good.)

LDA 的matlab源码实现，Jonathan Huang编写的，是目前唯一可用于且正确的源码，可用于图像特征降维(LDA to achieve the matlab source, Jonathan Huang, prepared, is the only can be used and the correct source, can be used for image feature dimensionality reduction)