shuzi

该代码实现序列图像配准和重建过程，包含多种算法，配准算法有keren,marcal等，重建算法包含了POCS凸集投影算法(The code sequence of image registration and renewal process, including a variety of algorithms, registration algorithms are keren, marcal etc. reconstruction algorithm contains POCS POCS algorithm)

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)

VC++,DirectX,运动图像检测,运动轨迹跟踪(VC++, DirectX movement trajectory tracking image detection)

使用regionprops中BoundingBox功能对图片进行处理获得X,Y的坐标值以及长宽度(Use regionprops the BoundingBox of the image processing functions to obtain X, Y coordinate values and long-width)

计算机视觉中得八点算法，采用SVD分解最小二乘解来求基本矩阵，这是模拟仿真实验程序。(Was 8:00 in computer vision algorithms, using SVD decomposition least-squares solution to seek the fundamental matrix, which is simulation, experimental procedure.)

行人检测HOG+SVM进行图片中行人检测，提供训练用的pos和neg样本，效果还可以；没有SVM工具箱的，压缩包里已经提供了，安装一下即可(Pedestrian detection HOG + SVM for pedestrian detection in pictures, providing POS and neg samples for training, the effect is good; without SVM toolbox, the compression package has been provided, just install it.)

傅里叶变换可以有效的说明傅里叶变换的基本步骤(kkkfuliyebianhuan)

目前最重要的图像变化检测评价标准：roc曲线，以及roc曲线下面积。(ROC curve of image change detection)

说明：  用matlab实现汽车牌照识别系统的定位算法(Automobile License Plate Recognition System Location Algorithm)

Object Recognition with Hierarchical Kernel Descriptors,In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.(Hierarchical Kernel Descriptors.)