toolbox_general

圆阵常规波束形成与MVDR用于方位估计的比较，可以更改信噪比仿真两者的区别联系(uniform circle array based for estimating direct of arrival,CBF and MVDR compared with adjustable SNR)

模糊核聚类算法的几篇文章还有其matlab的几个程序(Fuzzy kernel clustering algorithm several articles Matlab program)

基于OPENCV的SIFT特征提取与匹配算法。包含完整的从图像高斯金字塔、DOG、空间极值点提取、关键点描述、KDtree匹配等关键步骤的全部函数实现，对全面深入理解Lowe的SIFT算法有莫大帮助。程序运行前须安装（1）OpenCV: http://opencvlibrary.sourceforge.net （2）SIFT: http://web.engr.oregonstate.edu/~hess/index.html，并配置其环境参数。(OPENCV the SIFT-based feature extraction and matching algorithm. Contains a complete Gaussian pyramid from the image, DOG, space extremum point extraction, description of key points, KDtree matching key step in the realization of the full function of the comprehensive and in-depth understanding of Lowe s SIFT algorithm of tremendous help. Program to run before the installation of (1) OpenCV: http://opencvlibrary.sourceforge.net (2) SIFT: http://web.engr.oregonstate.edu/ ~ hess/index.html, and configure the parameters of their environment.)

织唛的缺陷检测，主要是运用surf算法进行检测。(Label defect detection)

将一张小图片影藏到大图片当中，使用半色调技术，并通过线性变换，调整两幅图像，将小图像影藏到大图像奇数行和列当中，可以调节线性函数的系数，改变影藏效果，改进后的(Will a small picture hidden to the big picture of a shadow of halftone use technology, and through the linear transformation, adjust the two images, the images hidden to big image of an odd number of rows and columns, can adjust the coefficients of the linear function, change the shadow hidden effect)

这是基于mean-shift运动目标跟踪的matlab仿真的程序，含有三个M文件(This is based on the mean-shift moving target tracking matlab simulation program, containing three M documents)

感兴趣区域提取，能用于车牌号及台风等图像，效果不错(Extract the interesting area, it can be used in license plate number and typhoons images and so on, has good results )

数字图像邻域平均法去噪的实现(包括有Ｍ文件和图像)(neighborhood Digital Image Denoising the average realized (including M documents and images))

MRI 读入k空间数据调整中心并进行傅里叶变换显示图像(MRI k-space data is read into the center and adjust the display image Fourier transform)

说明：  褶积模型正演模拟，非常好用，非常好用，非常好用！(Forward simulation of convolution model ,very good!)