weimin

segmentation tools for segmention lazy snaping

　计算机人脸识别技术( Face Reocgnition)就利用计算机分析人脸图像,从中提取出有效的识别信息,用来辨认身份的一门技术。[ 1 ]即对已知人脸进行标准化处理后,通过某种方法和数据库中的人脸样本进行匹配,寻找库中对应人脸及该人脸相关信息。人脸自动识别系统有两个主要技术环节,一是人脸定位,即从输入图像中找到人脸存在的位置,将人脸从背景中分割出来,二是对标准化后的人脸图像进行特征提取和识别。本文中介绍的PCA (特征脸)方法就是一种常用的人脸 特征提取方法。(Computer Face Recognition Technology (Face Reocgnition) on the use of computer analysis of facial image, to extract the valid identification information used to identify the status of a technology. [1] that is known to standardize treatment of face, through a method and a database of face samples for matching, search library, the corresponding face and the face-related information. Automatic face recognition system has two main technical aspects, first, face location, that is, from the input image to find the location of the face there, the faces will be split out from the background, the second is, the standard features of face images extraction and recognition. Described in this paper PCA (Eigenfaces) method is a common facial feature extraction method.)

A Matlab Toolbox for Solving Optimal Control Problems

copula中对边缘函数的说明copula的均匀分布的估计及Garch模型的拟合(The description of the edge function in copula)

相空间重构参数选择 延迟时间和嵌入维数(Phase space reconstruction parameters delay time and embedding dimension)

the action of denoiszing the actual noise from an system

VMProtect逆向分析相关的资料，一蓑烟雨论坛大牛的作品，对于破解或逆向分析很有启发作用。(VMProtect reverse analysis of relevant data, a domain misty rain BBS Daniel s work, enlightening for cracking or reverse analysis.)

《Matlab遗传算法工具箱及应用》这本书附带的的源码( Matlab genetic algorithm toolbox and its application, attached to this book s source code)

对于一个给定的离散随机序列，用间接法进行三阶累积量谱的估计，并分别用四种不同窗函数，包括Hamming窗、Sasaki窗、Parzen窗、daniell窗等。(For a given discrete random sequence, using the indirect method for third-order cumulant spectrum estimation, and were four different window functions, including the Hamming window, Sasaki window, Parzen window, daniell windows.)

围绕粒子群的当前质心对粒子群重新初始化.这样，每个粒子在随后的迭代中将在新的位置带着粒子在上次搜索中获得的“运动惯性”（wvi）向Pi，Pg的方向前进，从而可以在粒子群的运动过程中获得新的位置，增加求得更优解的机会.随着迭代的继续，经过变异的粒子群又将趋向于同一点，当粒子群收敛到一定程度时又进行下一次变异，如此反复，直到迭代结束.(particle swarm around the center of mass of the current PSO reinitialization. Thus, Each particle in the next iteration will be in the new location with particles in the last search was the "inertia" (wvi ) Pi, Pg orientation, and thus can PSO course of the campaign was a new position, increase seek better solutions opportunities. With the continued iteration, after variation of PSO will tend to the same point. When PSO converge to a certain extent when the next variation, so repeatedly, until the end of iteration.)