minimum-spanning-tree

DIX公式优化求解层速度，使得速度变得更加平滑。(DIX formula optimization solution velocity, making speed becomes smoother.)

几个大牛对集成了国内外特征提取最前沿的算法，特征提取，DOG,HARRIS,HARRIS-LAPLACE,HARRIS-AFFINE,MSER.描述符，SIFT,AFFINE-SIFT,GLOH.经测试，速度快，描述符准确。(Integration of feature extraction algorithms from several expert of domestic and international, feature extraction： DOG, HARRIS, HARRIS-LAPLACE, HARRIS-AFFINE, MSER； Descriptors： SIFT, AFFINE-SIFT, GLOH. The test reveal the speed and accurate descriptor.)

使用MRF进行SAR图像变化检测，能量最小化算法采用ICM(SAR image change detection uses MRF, energy minimization algorithm uses the ICM)

利用MATLAB实现了视频图像行人识别与检测，(Using MATLAB to achieve video image pedestrian recognition and detection,)

matlab_基于象素的图像配准程序,很好使用,精度也很高,要用的就下载吧!(matlab_ pixel-based image registration procedures, a very good use, accuracy is also high on the download you want to use!)

一种用于稀疏表示求解L1范数的算法，用L1范数近似L0范数获得了更好的效果。(A method for sparse representation algorithm for solving the L1-norm)

图片的旋转、截图、灰度、翻转、滤波、去噪等基本处理(Image rotation, screenshots, gray, flip, filtering, noise and other basic processing)

　　本函数选用阈值分割的方法来完成瞳孔定位。根据图像的灰度分布特性，瞳孔部分的灰度值最小，其次为虹膜部分，最大的为巩膜部分。因此在灰度直方图中，第一个大峰值的邻域即是瞳孔所在区域。(Function selects the threshold segmentation method to accomplish the pupil location. Based on image gray distribution features, the pupil part of the grey value of the minimum, followed by the iris part, the biggest part of sclera. So in gray level histogram, the peak value of the first big neighborhood is the pupil area.)

基于颜色的图像检索系统，带有两个图像数据库，采用分块图像检索方法。(Color-based image retrieval system, with two image databases, image retrieval method using block.)

说明：  PCA图像降噪新算法-LPG PCA，全名叫做Two-stage Image Denoising by Principal Component Analysis with Local Pixel Grouping。内含算法代码和数据集，均经本人实测，可进行灰度图像和RGB图像的降噪，且PSNR和SSIM指标明显好于小波滤波、K-SVD等经典算法。值得学习图像降噪的看看！(The new algorithm of PCA image denoising LPG PCA, full name is two stage image denoising by principal component analysis with local pixel grouping. The algorithm code and data set can be used to reduce the noise of gray image and RGB image, and the PSNR and SSIM are better than the classical algorithms such as wavelet filtering and K-SVD. It's worth learning about image denoising!)