Qu Bo Transform toolbox, which has some of the demo program to impatient, I hope...

用改进的GAC变分水平集方法实现图像分割，简化和避免了重新初始化问题-GAC improved variational level set method to achieve image segmentation, simplification and to avoid the issue of re-initialization

摄像头标定实现，基于opencv实现，需要先安装opencv-Camera calibration realize, based on the opencv realize the need to install opencv

基于Hough变换的火柴根数检测--Matlab7.0.4上开发-Based on the Hough Transform Detect root number of matches- Matlab7 .0.4 developed

使用Gabor滤波器组对图像进行卷积运算-use Gabor filters the image deconvolution operator

滤波器网上找到的在MATLAB中实现的可能有问题在这里和大家分享一下一起研究-Filters found in the online MATLAB realized there may be a problem here and everyone together to share research

图像处理中应用非常广泛的二维最小均方误差算法（TDLMS）。绝对好用！-Image Processing is widely used two-dimensional MMSE algorithm (TDLMS). Absolute ease of use!

HomoFilt.m 同态滤波器 ExGrey.m 灰度拉伸 Split.m 图象分割 ReadPic.m 读Pic图-HomoFilt.m homomorphic filter ExGrey.m tensile Split.m gray image segmentation ReadPic.m Fig Reading Pic

（1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 -(1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.

利用matlab编写的一个中值滤波源程序，希望对大家有用。-Matlab prepared using a median filter source, in the hope that useful to everyone.

磁共振成像经典算法sense，对于对并行成像的研究很有帮助-Magnetic resonance imaging classical algorithm sense, for the study of parallel imaging helpful