FibonacciHeaps

内容认证半脆弱水印算法的研究与实现,本程序实现图像的半脆弱水印，是基于DCT的算法，特别适合研究半脆弱水印的同志(Content authentication semi-fragile watermarking algorithm for Research and Implementation of, the procedures for the realization of the semi-fragile watermarking images is based on the DCT algorithm, especially appropriate to study the semi-fragile watermarking comrades)

说明：  基于GMM的快速点云的聚类分析的高斯混合模型，可用于模糊聚类(GMM Based Fast point cloud clustering analysis of Gaussian mixture model, which can be used for fuzzy clustering)

几种flexsim的模型（飞机场、公交线路、订单产生、农场）(Several models of Flexsim)

一个较好效果的彩色图像合成代码(分左右两个视窗,很好用(A good effect of color image synthesis code (about two Windows, very useful)

用matlab编程实现的POCS-TVM算法，重建了一幅大小180*180的Shepp-Logan头部模型图像，探测器个数260，投影角度为均匀发布在0-180度之间的60个角度。(The POCS-TVM algorithm based on MATLAB programming is used to reconstruct a Shepp-Logan head image with a size of 180*180. The number of detectors is 260, and the projection angle is 60 angles that are evenly distributed between 0-180 degrees.)

在数字图像处理中，灰度直方图是最简单且最有用的工具，可以说，对图像的分析与观察直到形成一个有效的处理方法，都离不开直方图。(in digital image processing, the histogram is the simplest and most useful tool, it can be said that the right image analysis and observation until the formation of an effective treatment method has been based on histogram.)

说明：  基于SegNet实现了车道线的识别。里面包含已经训练好的模型。(Lane line recognition based on SegNet contains the trained model.)

convert bmp to rgb565(algorithm to handle all kinds of bmp to rgb565)

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)

简单的graph cut 算法，初学者适合(Simple graph cut algorithm for beginners)