sar

我改写后的graphbrian类，原本支持柱状图饼图和折线图的，现在添加了对这些图的风格调整，主要是增加了调整绘图区背景色和文本背景的功能，还增加了把图片保存为bmp文件的功能，以便用其他软件方便的打印。(I would then graphbrian category, which originally supported the pie chart histogram and line graph, and now added to adjust the style of these plans, the main adjustment is to increase the drawing area background color and text background function, but also an increase of the picture saved as bmp files, in order to use other software to facilitate printing.)

基于Hough变换和基于Radon变换的直线检测算法代码(source code of two straight line detection algorithm (based on Hough Transform and Radon Transform))

一个非常有用的超像素算法，用于图像分割,文件夹包含文章,采用matlab和c混编(a very useful superpixels algorithm，can be used for image segmentation,the file contatins paper,the matlab and c are mixed for exeution)

This DataEntryFilter filters data entries based on whether their names match a given StringMatcher.

这是对6—PUS并联机器人的运动学仿真及运动轨迹跟踪(This is the kinematics simulation and trajectory tracking of the 6-PUS parallel manipulato)

一本讲述计算机图像和视频实时处理的好书。包括基本概念，硬件，软件，和算法。 (A book about real-time image and video processing, including basic concepts, hardware, software, and algorithms.)

说明：  SAR合成孔径雷达的点目标仿真报告，含matlab代码(SAR synthetic aperture radar point target simulation report, including Matlab code)

图像球面化，对于输入的图片进行球面变换达到图形变形的效果(Spherical image of, for input images to spherical transformation to achieve the effect of graph distortion)

自己编写的单应性矩阵的求法，按照张正友的论文进行编写的。希望有用。(I have written the homography method, according to Zhang Zhengyou papers prepared for. I hope useful.)

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)