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对最大类间方差法进行改良，引入最小类内方差，进一步提高分割效果(For improved Otsu method, introducing the minimum interclass variance, further improve the segmentation effect)

图像灰度化处理、图像柔和处理、图像锐化处理(Grayscale image processing, image gentle processing, image sharpening)

基于熵率的超像素分割代码，是顶级会议上的代码(Entropy Rate Superpixel Segmentation)

自适应中值滤波器 该滤波器与中值滤波器相比，功能有很大的改善，但函数要复杂了很多(adapt median filter ,this filter compared with median filter, it is much improved in the resulted image.)

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 )

求图像的二维直方图，产生共生矩阵，可以比较像素点分布的是否均匀。(Two dimensional histogram for image, the co-occurrence matrix, can compare the pixel distribution is uniform.)

直方图调整并显示对比，直方图调整后都需要对比一下调整前与调整后的区别，并显示。此程序适合初学者学习。(Histogram and adjust the display contrast)

视频图像处理，采用背景减除法中混合高斯建模进行视频前景和背景的分离(Video image processing, background subtraction method using Gaussian mixture modeling to separate the foreground and background video)

利用相位相关法实现了图像的匹配，这种方法的优点是提取速度快，方法简单，运算量少。(Using the phase correlation method to realize the image matching, the advantage of this method is the extraction method is simple, fast, less computation.)

二维相位展开算法 引导相位展开方法相质量运行QualityGuidedUnwrap2D的。 运行戈尔茨坦枝剪相位展开方法GoldsteinUnwrap2D。 （3D实施质量的制导方法，可根据要求提供）(2D PHASE UNWRAPPING ALGORITHMS Run QualityGuidedUnwrap2D for the phase quality guided phase unwrapping method. Run GoldsteinUnwrap2D for Goldstein s branch cut phase unwrapping method. (3D implementation of the quality guided method available on request))