codes3_GUI

基于分数阶傅里叶变换的五篇博硕士论文，分别是在数字水印中的应用两篇，图象变换中的应用一篇，滤波中的应用一篇，目标检测中的应用一篇。(Based on Fractional Fourier Transform five Theses, respectively, in the digital watermark in the application of 2, images in the application of a transformation, filtering in the application of a target detection application 1.)

TV正则化去卷积the Iteratively Reweighted Norm algorithm for solving the generalized TV functional, which includes the L2-TV and and L1-TV problems(An Iteratively Reweighted Norm Algorithm for Minimization of Total Variation Functionals)

这是一个经典的马尔科夫场程序。算法详细，而且里面含有一篇与程序对应的文献，解释非常详细，程序非常好用(This is a classical Markov field program. Algorithm is detailed, but it contains a corresponding literature and procedures, explaining in great detail, the program is very easy to use)

echarts-master  

k均值图像分割，经典的图像分割方法，算法简单，效果好。(k-means image segmentation)

求解L1范数最小化问题的凸优化工具包，共含有6个模型的求解方法(Solving the L1-norm minimization problem of convex optimization toolkit contains a total of six methods of solving the model)

实现不规则形状碎纸片的拼接，并展示出原图像(The splicing of irregular shaped pieces of paper is realized and the original image is displayed.)

RAw格式图片的显示，边缘检测，包括SOBEL算子，具有不同模板的拉普拉斯算子边缘检测，以及一些简单的滤波方式(sadfsadf asdfasdf asdf)

VC计算机图形学原理及算法教程 VC计算机图形学入门教程(VC theory and algorithms of computer graphics computer graphics tutorial Tutorial VC)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)