gmm

一种改进的基于蚁群算法的图像边缘检测文献，前沿论文(ant colony system,matlab)

程序设计步骤： 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量(Programming steps: 1 to mean 2, calculation of covariance matrix and its eigenvalues and eigenvectors 3, the calculation of covariance matrix eigenvalues greater than the threshold number of 4, in descending order of eigenvalue 5, remove the smaller eigenvalue 6, remove the larger eigenvalue (usually not this step) 7, the combined choice of eigenvalues 8, select the appropriate eigenvalues and eigenvectors 9, computing whitening matrix 10, principal component extraction)

该上传的256张CT图片是高质量的DICOME格式人类头骨的连续序列切片图，可直接用于医学三维重建或体绘制。(The upload of 256 CT images are of high quality DICOME form a continuous sequence of the human skull slice diagram can be directly used in medical 3D reconstruction or volume rendering.)

pca-sift算法的实现，采用vc2008开发环境，进行开发，独立运行(pca-sift algorithm, using vc2008 development environment to develop, operate independently)

说明：  是马尔可夫图像分割的C++程序，可以用于遥感图像(It's a C++ program for Markov image segmentation, which can be applied to remote sensing images.)

vc++ 数字图象处理 很好用我看了以后收益非浅希望大家喜欢(vc digital image processing with very good after I read greatly benefit hope you like)

自己编写的数字图像处理程序，包括打开bmp图像，直方图，阈值分割，形态学处理等功能。(digital image processing procedures, including open bmp image, histogram, threshold segmentation, morphological processing and other functions.)

遥感摄影测量专业相邻相片同名点之间的匹配，交互界面，可以人工选点(Remote Sensing Photogrammetry Professional adjacent matching interface point between the photo of the same name, you can manually choose the point)

图像处理的简单程序，适合初学者，自己好好看看(Simple image processing procedures, suitable for beginners, its a good look)

该源码为目标检测和跟踪算法 包括： 1. 静态背景下的背景预测法目标检测 2. 静态背景下帧间差分法目标检测 3. Mean Shift目标跟踪方法 4. 重心多目标跟踪方法 (The source code for target detection and tracking algorithm include: 1 Static background background prediction target detection 2 Static background frame difference method for target detection 3. Mean Shift tracking method 4 multi-target tracking methods focus)