Three dimensional vascular reconstruction

可实现对残缺图像的修复，应用了基于mp算法的图像稀疏分解思想。过程中考虑到了原子库原子的能量特性......(Enables the restoration of fragmentary images, applications mp-based algorithm for image sparse decomposition ideas. The process of taking into account the characteristics of atomic energy of the atom library ......)

可以进行背景减除法-三帧法-高斯背景建模法运动检测( This file (the project file) contains information at the project level and is used to build a single project or subproject. Other users can share the project (.dsp) file, but they should export the makefiles locally. )

MATLAB工具sift实现图像拼接，上下拼接(MATLAB tool sift image stitching, stitching up and down)

图像拼接，用于合成全景图像，VC++开发环境，C++实现。(Image Stitching)

亚像素提取边缘源代码，可以进行准确的边缘检测(Subpixel edge source code)

FCM是一种实用的算法，在医学图像分割中被广泛应用，有很多的改进措施，本程序是基于FCM来分割MRI人脑图像(MRI brain segmentation using FCM)

对最大类间方差法进行改良，引入最小类内方差，进一步提高分割效果(For improved Otsu method, introducing the minimum interclass variance, further improve the segmentation effect)

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)

灰度图像的属性计算：利用bwlabel和regionprops函数对度量二值图像中目标对象的属性。(Attribute gray image: Using bwlabel and regionprops function measure binary image of the target object attributes.)

图像高斯模糊的算法,自定义大小和模糊半径(psf of Gaussian)