svt

说明：  在autocad2007环境中直接使用API就可以亮显实体的一个示例演示(This program demonstrates how to use ObjectARX API functions to highlight and unhighlight subentities of complex entities such as ACIS solids.)

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)

通过erdas遥感软件提取水体的三种操作(Extracting water through software operation of the three operating)

数字图像处理实验——直方图的均衡化，打开图像并实现其均衡化(Digital image processing experiments- histogram equalization, open the image and realize its equalization)

采用大津阈值法检测车道线，效果还可以，有需要的可以看看(Using the Dajin threshold method to detect the lane line, the effect is still good, you can see if you need it.)

有关pca算法方面的学习，有利于pca算法原理的实现(something related to the study of pca)

压缩感知中的梯度投影重构算法，经修改可以运用与单像素相机成像(Gradient projection reconstruction algorithm of compressed sensing, modifications can be used with single pixel camera imaging)

k-均值聚类算法实现灰度图像分割，输入图像矩阵和聚类中心个数，返回为最终的聚类中心和图像中每个像素所属类的编号（对应于图像矩阵）(k-means clustering algorithm to achieve gray-scale image segmentation, the input image matrix and the number of cluster centers, the return for the final image of the cluster centers and their respective categories in each pixel number (corresponding to the image matrix))

说明：  用FCM算法实现图像分割(图像源可以是灰度、索引和RGB图像)(FCM image segmentation algorithm (image source can be gray, indexes and RGB images))

基于马尔科夫随机场的SAR图像分割方法，对应于采用最大后验概率准则对SAR目标切片图像分割，采用聚类分析算法求解。(SAR image segmentation based on Markov random fields, which corresponds to using the maximum posterior probability criteria for SAR Target Chip Image segmentation using cluster analysis algorithm for)