sift_0_9

利用matlab的gui设计的带有输入和输出功能的界面图形，可视性很强，学习非常值得(The interface graphics with input and output functions designed by GUI of MATLAB have strong visibility and worth learning.)

自己写的中值滤波剔野程序，用于信号野值剔除。很简单，其中算法可替换(Write your own wild tick median filtering program for the signal, excluding outliers. Very simple algorithm which can be replaced)

说明：  SUSAN得源代码 希望对你有用 SUSAN得源代码(SUSAN source code in hopes SUSAN useful to you in source code)

一个卫星绕地球转的程序。。。。。。。。。。(A satellite around the earth' s rotation program)

利用径向基函数的方法进行隐式表面重建，matlab代码(Radial basis function method for implicit surface reconstruction)

说明：  Fienup的混合输入输出算法，是一种迭代算法，用来求相位的算法HIO(Fienup's hybrid input and output algorithm is an iterative algorithm used to find the phase algorithm HIO)

说明：  对文字识别并计算7个不变矩的matlab源程序(right words to identify and calculate seven unchanged Moments Matlab source)

sift算法0.9版；对数据进行提取后聚类；采用聚类算法k-means(sift algorithm version 0.9 right after extracting the data clustering using k-means clustering algorithm)

图像配准，首先实现了简单的图像预处理，然后根据两种不同的配准准则，互信息和边交互方差作图像配准(Image registration, first of all to achieve a simple image pre-processing, and then two different registration criteria, mutual information and edge interaction variance for image registration)

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)