SegTool-lazy

detection different object in video an moves with high accuracy

说明：  计算两个离散变量x和y的联合熵z = H（x，y）。 输入：x，y：两个相同长度的整数向量输出：z：联合熵 可以使用，有利于学习目的(Compute joint entropy z=H(x,y) of two discrete variables x and y. Input: of x, y: two integer vector of the same length Output: z: joint entropy good for learning purposes)

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 )

格子波尔兹曼来实现图像修复，能够快速高效的实现图像修复(Lattice Boltzmann for image restoration, fast and efficient image restoration)

说明：  基于matlab的钢筋计数方法，有ui界面(MATLAB-based steel bar counting method with UI interface)

视频水印matlab源码，此为第五个压缩包，在水印领域的爱好者可以下载作为参考(Video Watermarking Matlab source, which is the fifth-compressed, Watermark in the field of lovers can download as a reference)

RAC法标定程序，适用于单目摄像机的线结构光的标定(The RAC calibration method procedure applies to line structured light calibration)

说明：  利用非局部均值的算法对图像做去噪，速度比较快，但效果一般，存在一定局限性(Using non-local mean algorithm to denoise image is faster, but the effect is general and there are some limitations.)

目标分类（流形学习slle+支持向量机svm）(Target classification (manifold learning slle+ support vector machine svm))

读入光流估计的结果，将运动矢量转化成颜色，生成颜色的光流估计图像。(Read optical flow estimation result, the color motion vector conversion, optical flow estimation to generate a color image.)