bianyuantiqu

一个有关边缘检测 robert sobel FFT傅里叶变换等算法的demo(a demo using VC++ that implements the algorithms of edge detection and FFT)

说明：  关于全变分的图像修复算法。。 。。 。。 。。 (On the whole variational image restoration algorithm. . . . . . . .)

基于MRI的脑组织分割。首先从给定的MRI图像中得到脑区域，而后计算脑容积；再得到脑白质灰质区域，并计算百分比。(Given an MRI scan, first segment the brain mass from the rest of the head, then determine the brain volume. Also compare portions of gray and white matter present. )

用matlab实现了分形，效果还是不错的，是分形学习中的基础内容(Matlab realize the use of fractal, the effect is still good, is a fractal study based on content)

说明：  冈萨雷斯数字图像处理matlab版源码DIPUM Toolbax(Gonzalez digital image processing matlab version of the source DIPUM Toolbax)

编程实现了抑制sar图像相干斑噪声的精致lee滤波算法，效果很好(refined lee)

图像线性插值Verilog代码，已通过FPGA验证(Image linear interpolation Verilog code, has been verified by FPGA)

说明：  该课题为基于MATLAB的汽车出入库识别系统，带有丰富的人机交互GUI界面。目前毕业设计选题中，传统的中规中矩的车牌识别不易得到高分，必须要在此基础上有所创新方得可以避开其他雷同课题，不会轻易被导师被否决而导致毕设不过。因此建议在车牌识别基础上加入多种方法的对比，看看哪种方法的精度高。虽然目前有很多方法的车牌识别，但是都是在各自的测试库里面测试的，比如测试的车牌图像不同，字符库不同，导致无法做对比。整个设计在一个GUI界面上完成。(This project is based on MATLAB vehicle in and out of the warehouse identification system, with rich human-computer interaction GUI interface. At present, in the topic selection of graduation project, the traditional regular license plate recognition is not easy to get high scores, so it is necessary to innovate on this basis to avoid other similar topics, and it will not be rejected easily by the tutor, leading to the failure of graduation project. Therefore, it is suggested to add a variety of methods on the basis of license plate recognition to see which method has high accuracy. Although there are many methods of license plate recognition, they are tested in their own test library, for example, the test license plate image is different, the character library is different, so it can not be compared. The whole design is completed on a GUI interface.)

简单的图形文字识别软件，希望大家喜欢我的分享，谢谢(Simple graphical text recognition software, I hope you like my share, thank you)

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)