NCSA

关于自适应粒子滤波，我觉得很好用，贝叶斯滤波适合非线性和非高斯环境，在粒子滤波基础上 又加改进(About adaptive particle filter, I feel very good, a bayesian filter for nonlinear and non-gaussian environment, particle filter in again on the foundation and improvement)

用小波包分析实现轴承故障诊断，信号分析处理，界面编程(Bearing fault diagnosis using wavelet packet analysis and signal analysis and processing, interface programming)

说明：  基于opencv的图像灰度化，将24位彩色图像重新量化为8位灰度图像(gray image)

说明：  一个基于样例的图像修补技术的matlab演示(A sample-based image repair technology demo matlab)

说明：  图形处理功能，有滤波功能，边缘提取等各个方面，值得一看(Graphics processing capabilities, has filtering, edge detection and other aspects, see)

说明：  用matlab将图片转成灰度图TXT，再通过verilog将数据导入FPGA中，采用高斯滤波算法来处理，再将处理后的图片数据导出到TXT中。(The image is transformed into gray-scale image TXT by MATLAB, and then the data is imported into FPGA by Verilog, processed by Gauss filter algorithm, and then the processed image data is exported to TXT.)

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)

说明：  吴恩达第五周作业。学习算法分两部分进行理解，第一部分是根据code对算法进行综述，第二部分是代码(The learning algorithm is divided into two parts, the first part is to summarize the algorithm according to the code, the second part is the code)

book of image fusion

说明：  经典图像处理灰度图像去噪模型，以及改进模型效果不错(Classic image processing gray image denoising model, and improved model good results.)