Reversible-New-Code

说明：  Matlab编写的程序代码，包括基于肤色的人脸检测，根据人脸图像判别眼睛的状态（睁眼及闭眼）(Matlab program written in code, including color-based face detection, face image in accordance with the state of discriminant eyes (eyes open and eyes closed))

数字图像插值增减算法。数字图像插值增减算法。数字图像插值增减算法 (reduce)

用matlab实现两幅彩色图像的无缝融合，输出结果为灰度图象(Using two color images matlab realize seamless integration, the output for gray-scale image)

关于elastix医学图像配准算法库的例子程序，有助于分析elastix如何操作，是一个很好的教程(this is a example set of elastix library)

传统的几种图像增强与去噪方法，包括直方图处理，灰度变换，滤波去噪等几类。细分为直方图均衡化，直方图规定化，灰度线性变换，中值滤波，维纳滤波，同态滤波(The traditional methods of image enhancement and denoising include histogram processing, grayscale transformation, filtering and denoising. Subdivided into histogram equalization, histogram specification, gray level linear transformation, median filtering, Wiener filtering, homomorphic filtering)

图像的各向异性扩散，利用matlab实现Perona_Malik算法，梯度阈值K通过对图像求解2-范数的方法迭代获得(Anisotropic diffusion images, using matlab to achieve Perona_Malik algorithm, the gradient threshold images by solving the K 2- norm method will be obtained)

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 )

带通配符的字符串匹配,多重匹配,支持通配符“*”，“？”(String matching with wildcards, multiple matches, supports wildcards " *" , " ?" )

这是本人自己实现的椭圆检测算法，基于MATLAB实现，经测试对一些图片椭圆检测效果较好，需要同学可以参考(This is my own implementation of ellipse detection algorithm based on MATLAB implementation, tested on a number of pictures ellipse detection is better, students can refer to the need to)

基于形态学的在Matlab下进行的图象骨架化及恢复,其中包含骨架化函数Frame和恢复函数refresh(based on the morphology of Matlab under the images of the skeleton and the restoration, which includes Frame function of the skeleton and the restoration of function refresh)