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这是基于图论的图像分割源代码，效果很好，欢迎下载.(This is based on graph theory image segmentation source code, works well, welcome to download.)

亚像素位移和变形梯度测量采用数字图像/散斑相关(Subpixel displacement and deformation gradient measurement using digital image/speckle correlation(„ DISC… ))

用C写的图像融合代码，包括了医学图像融合、多焦点图像融合和多传感器图像融和等多种类型的融和算法，运行速度快。(Image fusion with the C written code, including the medical image fusion, multi-focus image fusion and multi-sensor image fusion and many other types of fusion algorithms, to run fast.)

通过非监督分类中的K-均值聚类对Indian pines高光谱图像进行分类(Classification of hyperspectral images of pines Indian by K-means of non supervised classification)

Level set solver for two phases flow

用matlab实现了暗通道图像增强，完整源代码，出自暗通道增强算法的提出者。(Dark-channel image enhancement, complete source code from the authors of the dark channel enhancement algorithms.)

一个老外用matlab 编写的5点法图像相对定向程序，是目前最新的算法实现。(5 point to prepare for a foreigner relative orientation program)

说明：  生成显著图方法，一种基于空间频域分析的显著性分析算法（Spectral Residual，SR算法）的matlab实现代码(Matlab code generated saliency map method based on the spatial frequency domain analysis of significant analysis algorithm (Spectral Residual, SR algorithm))

WMIL跟踪算法的实现,代码注释全功能强大(WMIL tracking algorithm, code comments all powerful)

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)