Fivepoint

求图像在hsv颜色空间下三个通道的颜色矩(colormoment in hsv colorspace)

最大熵直接迭代法,维纳滤波和盲恢复对水平方向匀速直线运动造成的模糊图像的恢复研究(Direct iterative maximum entropy method, Wiener filtering and blind restoration of the horizontal direction caused by uniform linear motion blurred image restoration)

说明：  用python实现傅里叶的快速变换，实现与传统傅里叶变换不同的快速傅里叶变换，也使用了一些图形界面设计(Using Python to realize the fast Fourier transform, which is different from the traditional Fourier transform, and also using some graphic interface design)

三维人脸数据库FRGC V1. 0,2.0包括图像的深度数据和纹理，格式分别保存为：abs和ppm 读取abs数据中的深度z轴数据，保存为480*640的矩阵，进而进行数据处理研究。并且贴ppm纹理，便于观察。 (Three-dimensional face database FRGC V1. 0,2.0 depth data including images and textures, formats were saved as: abs abs data and ppm reads depth z-axis data, save it as a 480* 640 matrix, and then studied for data processing . And paste ppm texture, easy to observe.)

本程序包是关于图形处理和人脸识别的,包括人脸Gabor特征提取，canny算子，水线阈值方法等．大家可以一起参考(The package is on the graphics processing and face recognition, including face Gabor feature extraction, canny operator, waterline threshold methods. Together we can make reference)

三维激光点云配准是点云三维建模的关键问题之一。经典的 ICP 算法对点云初始位置要求较高且配准 效率较低，提出了一种改进的 ICP 点云配准算法。该算法首先利用主成分分析法实现点云的初始配准，获得较好 的点云初始位置，然后在经典 ICP 算法的基础上，采用 k － d tree 结构实现加速搜索，并利用方向向量夹角阈值去除 错误点对，提高算法的效率。实验表明，本算法流程在保证配准精度的前提下，显著提高了配准效率。 (Three-dimensional laser point cloud registration is one of the key three-dimensional point cloud model. High classical ICP algorithm to the initial position of the point cloud registration requirements and low efficiency, proposed an improved ICP point cloud registration algorithm. Firstly, the use of principal component analysis of the initial point cloud registration, get a better initial position of the point cloud, then the basis of classical ICP algorithm using k- d tree structure to achieve speed up the search, and using the direction vector angle the removal of the threshold point error and improve the efficiency of the algorithm. Experiments show that the algorithm processes to ensure the accuracy of registration under the premise, significantly improve the efficiency of registration.)

discrete hartley trnasform paper

sift算法在cv领域的重要性不言而喻，该作者的文章引用率在cv界是number1.本篇博客只是本人把sift算法知识点整理了下，以免忘记。本文比较早的一篇博文opencv源码解析之(3)：特征点检查前言1 中有使用opencv自带的sift做了个简单的实验，而这次主要是利用Rob Hess的sift源码来做实验，其实现在的opencv版本中带的sift算法也是Rob Hess的，只是稍微包装了下。 下面来做下试验,试验sift代码采用Rob Hess的代码，opencv目前版本中的sift源码也是采用Rob Hess的。代码可以在他的主页上下载：http://blogs.oregonstate.edu/hess/code/sift/ 这里我下载的是windows版本的，并采用Qt做了个简单的界面。(sift algorithm is self-evident the importance of the cv field, the author of the article referenced in the cv sector is number1 this blog I just sift algorithm knowledge compiled under, so as not to forget. This article compares the earlier blog post opencv source code analysis (3): check the preface of the feature points 1 the opencv bring their own sift to do a simple experiment, but this time Rob Hess' s sift source to experiment. In fact, with the opencv version sift algorithm is Rob Hess, only slightly packing the next. Below to do the next test, test sift code using the code of Rob Hess, sift source opencv version of Rob Hess. The code can be downloaded on his home page: http://blogs.oregonstate.edu/hess/code/sift/ here I downloaded the windows version, and using Qt to do a simple interface.)

根据厄米高斯光束的振幅分布物理公式来画厄米高斯光束的强度分布图(plot the intensity distribution of the HermiteGaussian Beams based on the physical formulars of the Hermite-Gaussian Beams )

这是一部关于非高斯噪声中信号检测与估计的经典书籍，springer出版社出版，作者是S Kassam(This is a classical book about signal detection in non-Gaussian noise, by S Kassam)