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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.)

一个快速而精确的一阶稀疏图像复原算法。主要应用于图像去噪声。(A fast and accurate first-order sparse image restoration algorithm. Is mainly used in the image to the noise.)

说明：  此源码可对图像进行Ncut分割，并且集成了特征提取和SVM分类的功能。(This source can be Ncut image segmentation, and integrates the feature extraction and SVM classification function.)

说明：  在这个知识库中，图形信号处理的一些有趣的特性被表示出来。演示通过经典信号处理和图形信号处理对一维和二维欧几里得域信号应用低通滤波器，比较两种方法的结果。在此基础上，验证了图形信号处理的工作机制。此外，还描述了在非欧几里得域上滤波信号的示例。(In this repository, Some fascinating features of Graph Signal Processing were represented. Demos incudes applying a low-pass filter on both 1D and 2D euclidian domain signal by classical signal processing and also Graph signal processing to compare both results are the same. Within that way, we will validate the working mechanism of Graph Signal Processing. In addition to this, an example of filtering a signal on the non-euclidian domain was also represented.)

DISKREET: FOR CHANGING DISKREET LEVEL OF AN IMAGE

说明：  Make_chaos 将图像进行置乱，生成乱序图，以便进行水印嵌入(Make_chaos will Scrambling images, generate disorder plans for embedding)

MATLAB程序；图像的小波系数提取，然后进行奇异值分解，得到图像的特征系数。(MATLAB program wavelet coefficients of the image extraction, and singular value decomposition, image feature coefficients.)

将原图像进行退化处理（选择大气湍流导致图像模糊时相应的PSF），然后将退化后的图像进行复原（使用维纳滤波、直接逆滤波）.(The original image degradation process,and then restore the degraded image with Wiener filtering and inverse filtering.)

图像的检测消失点，运用到边沿检测，霍夫变换等(vanishing point detection)

New Tracking Algorithm for Wireless network