svm

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)

利用hough变换检测圆，有RHT，随机hough变换，以及一些改进(Using Hough transform to detect the circle)

互信息和梯度相结合的图像配准算法，在配准中引入了图像的空间信息(Mutual information and gradient combination of image registration algorithm, the introduction of registration in the image of the spatial information)

比较反滤波和维纳滤波对一幅图像复原的效果。从两方面出发，一是对运动模糊和均值模糊复原的能力，二是抗噪能力，各种情况分别都进行了对比(Comparison of inverse filtering and Wiener filtering on an image restoration results. Proceed in two ways, first on the motion blur and the mean fuzzy recovery capability and noise immunity, all cases are compared separately)

说明：  采用双相位编码对图像进行加密解密，采用双随机相位加密技术得到的 S 分量的相息图, 在加密的过 程中引进了随机相位因子, 在不知密钥的情况下解 密出 S 分量几乎不可能, 从而保证了的安全性(Two phase encoding is used to encrypt and decrypt the image)

说明：  bp人工神经网络模式识别教程示例，研究人脸识别 手势识别有帮助(bp nervel network trainning and recognition ,e.g:face & dynamic hand gesture detection and face recognition .I hope it would be very useful for researcher !)

Sobel算子就是一种基于方向导数掩模求卷积的方法。通过边缘检测算子可以检测到每个像素的领域并对灰度变化率进行量化，也包括方向的确定。该代码是基于sobel算法的检测程序。(Sobel operator is a directional derivative mask and convolution methods. To each pixel and gray level in the field rate of change can be detected through the edge detection operator to quantify, including direction. The code is based on sobel algorithm testing procedures.)

orthogonal Matching Pursuit(OMP),CoSaMP算法的Matlab实现。(orthogonal Matching Pursuit(OMP),CoSaMP Algorithms)

基于mean-shift的动态目标跟踪算法，简单有效，另附图库(Based on mean-shift of the dynamic target tracking algorithm, simple and effective, attach library)

一些基本的图像处理算法的matlab实现，包括边缘提取和roc分析等(some basic image processing algorithms to achieve Matlab, including edge detection and analysis roc)