matlab背景差分法

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)

医学图像配准，医学图像2D_3D配准，基于vc++开发。(The registration of pre-operative volumetric datasets to intra-operative two-dimensional images provides an im- provedway of verifying patient positionandmedical instru- ment location. In applications from orthopedics to neuro- surgery, it has a great value in maintaining up-to-date in- formation about changes due to intervention. We propose a mutual information-based registration algorithm which es- tablishes the proper alignment via a stochastic gradient as- centstrategy. Ourmaincontributionliesinestimatingprob- ability density measures of image intensities with a sparse histogrammingmethodwhichcouldlead to potentialspeed- up over existing registration procedures and deriving the gradient estimates required by the maximization procedure. Experimental results are presented on fluoroscopy and CT datasets of a real skull, and on a CT-derived dataset of a real skull, a plastic skull and a plastic lumbar spine seg- ment.)

各类图形处理算法代码，齐全的图形处理算法，可以直接运行。去噪，识别，检测，拼接等(Complete graphics processing algorithm, you can run directly. Denoising, identification, detection, stitching and so on)

褶积模型 正演模拟 子波与反射系数合成地震响应(model zhengyanmoni zhejimoxing)

指纹图像预处理、特征提取、特征匹配的matlab程序及处理结果(Fingerprint image preprocessing, feature extraction, feature matching matlab program and processing results)

说明：  WAV音频的MATLAB读取以及傅里叶变换作频谱图(WAV Audio Reading by MATLAB)

SIFT特征提取 http://www.opencvchina.com/forum.php?mod=viewthread&tid=209&fromuid=267 (SIFT feature extraction http://www.opencvchina.com/forum.php?mod=viewthread& tid=209& fromuid=267)

基于CFA（颜色滤波阵列）插值检测的图像篡改检测程序(Image tamper detection based on CFA (color filter array) interpolation detecting.)

求解声压级，总声压，三分之一倍频程，倍频程，能量谱密度(get one third octave)

写成gauss-gamma双窗函数，计算含有相干斑噪声的sar图像的梯度(The gauss-gamma double window function is used to calculate the gradient of the SAR image with the speckle noise)