M_nlms

对细胞进行分割和追踪的代码，细胞是植物细胞，其中的两篇论文是局部图分割的经典论文，值得一看(To segmentation and tracking code of cells, cells is plant cell, two of the paper is a classic papers of local map segmentation, worth a look)

这个是广义高斯分布随机数产生程序，它可以用做广义高斯分布形状参数的估计，以及用在图像处理，独立成分分析，信号处理，数字水印等方面。(This is the random numbers about the generalized Gaussian distribution.It can be used in image processing, ICA, signal processing,digital watermark and so on.)

极化SAR图像多纹理最大似然处理滤波算法，可以生成处理后的四个通道图像数据。(Multi-polarization SAR image texture filtering deal with maximum-likelihood algorithm, can be generated after the deal with the four-channel image data.)

说明：  用于三维空间坐标系变换的7参数模型，在设计坐标系变换的各行业应用中用的最多，从数学角度来说也是最严密的转换方法。 由于结果中最多可求得七个转换参数，即三个平移参数、三个旋转参数(Ex、Ey、Ez)和一个尺度缩放因子(m)，因此，通常也被称为七参数法。 (Three-dimensional coordinate system transformation for the 7 parameter model, coordinate system transformation in the design of applications using the most, the industry, from the mathematical point of view, the conversion is the most rigorous methods. Since the results can be obtained up to seven transformation parameters, ie three translation parameters, three rotation parameters (Ex, Ey, Ez) and a zoom scale factor (m), therefore, often referred to as seven-parameter method.)

说明：  图片马赛克效果图片马赛克效果图片图片马赛克效果马赛克效果(Photo mosaic picture image photo mosaic mosaic mosaic)

自己编写的二维傅里叶变换的源代码，并在变换后显示频谱。(Two-dimensional Fourier transform of the source code I have written, and displayed in the transformed spectrum.)

可以实现bmp图象的sobel边缘检测运算，并含有可图象重建功能。简单，易懂，使用了DIB接口，适合作数字图象处理的初学者作参考(Bmp images can be achieved sobel edge detection operator, and contains the reconstruction of functional images. Simple, understandable, the use of the DIB interface, suitable for beginners of digital image processing for information)

激光光束的整形，用于将高斯光束整形成均匀光束，采用GS算法(Laser beam shaping, for forming a uniform whole beam Gaussian beam using GS algorithm)

混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。(Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of)

压缩感知的快速Laplace恢复算法,算法适用于超完备字典(the Beyes recovery algorithms are similar to the sparse coding based on the over-complete dictionary. )