MATLAB(DCT+DWT)

主要功能库存管理系统主要由1 货物管理2 基本档案3 查询统计4 系统维护5 帮助等模块组成进入该系统后，用户可以对系统中的一些基本信息进行添加、修改和删除等操作。另外，如果是管理员登录，还可以对用户的权限、用户名和密码进行修改。

用于matlab图像处理,计算向量均值C，协方差m。 [C,m]=covmatrix(X)(Calculate vector mean C, covariance M.)

完整的程序，提供测试视频帧。根据视频帧中图像的形状进行运动估计(integrity of the procedures for testing video frames. According to Video Frame Image Motion Estimation shape)

先用双峰法将细胞图像进行二值化，然后进行分割和癌细胞识别(Bimodal method first cell image binarization, and segmentation and identification of cancer cells)

利用梯度插值算法实现图像的去马赛克，图像质量大大提高(Using gradient interpolation algorithm to achieve image mosaic, image quality is greatly improved)

数​ 字​ 图​ 像​ 处​ 理​ 中​ 的​ b​ p​ m​ 图​ 像​ 读​ 取(Digital image processing of the image b p m reading)

实现图像渐晕效果的模拟，输入一副分辨率大于800×500的bmp图像，可以得到模拟出渐晕效果后的图像，参数可调(The image vignetting effect simulation, input a bmp image resolution is greater than 800,500, and adjustable parameters can be simulated image vignetting effect)

对比CPU和GPU加速，pcl::cuda的使用教程，利用随机采样一致(RANSAC)去除地平面等例子。(Compare CPU and GPU acceleration, pcl::cuda tutorial, using random sampling consistency (RANSAC) to remove ground plane and other examples.)

量子图像加密 模拟BB84规则 对图像进行加密(quantum signal processing)

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)