geo_feature_image

说明：  利用双线性插值法，来对图像进行放大或者缩小的操作(Bilinear interpolation is used to enlarge or shrink the image)

基于YCgCr色彩空间与几何信息的人眼定位方法(YCgCr color space and geometry based on the information the human eye location methods)

使用camshift方法进行运动目标跟踪，（修改了网上原有的camshift算法里的一些bug）(moving object tracking by camshift method,(modification edition of the original code on the web))

Complete class-based image processing, contains all of the source code, auto image, There are cycle detection, periodic testing, MIT Artificial Intelligence Laboratory identification of the target source.

有较好的参考价值，进行波形数据分析,实现六自由度运动学逆解算法。( There are good reference value, Waveform data analysis, Six degrees of freedom to achieve inverse kinematics algorithm.)

斑点抑制各向异性扩散滤波（SRAD）算法 的中文论文(Speckle suppression anisotropic diffusion filter (SRAD) algorithm)

通过区域迭代合并与定位切图的图像分割算法，非常好的。(Image Segmentation by Iterated Region Merging with Localized Graph Cuts)

三种基于GraphCut的图像分割方法，分别是GrabCut，LazySnapping和改进的GrabCut，其中用到了matlab和VC++的混合编程，在调用之前需生成mex文件(Three kinds of image segmentation method based on GraphCut, respectively GrabCut, LazySnapping and improved GrabCut, which used the matlab and VC++ mixed programming, before calling the mex file to be generated)

一种基于半监督的svm的图像分类方法。该方法通过聚类核的方法利用无标记样本局部正则化训练核的表达式。这种方法通过图像直接学习一个自适应的核。该程序仿真的是文章：Semi-supervised Remote Sensing Image Classification with Cluster Kernels。大家可以参考下。(A semi-supervised SVM is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image, and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictionsds)

labview基础视觉识别的东西，讲的很细(The basic visual recognition course of LabVIEW is very detailed.)