MATLAB

一个老外开发的地质统计学matlab工具箱,具有较全的统计算法如克里金(A foreigner and development of geostatistics matlab toolbox, has the full statistical algorithms such as kriging)

文件1 txt,2 txt,3 txt和5 txt为用Fortran编写的有限元程序 4 txt为用c＋＋编写的钢筋混凝土异形柱的全()

这个程序实现的是任意一个输入波长转化它的RGB分量，是由著名的色度图上转化而来(This program is to achieve an arbitrary wavelength of an input into its RGB components is determined by well-known color chart conversion from)

SAR图像和光学图像的配准算法！！！matlab源码~~~~绝对好！(SAR image and optical image registration algorithm! ! ! matlab source code ~ ~ ~ ~ definitely good!)

此程序用matlab实现了在水果图片中识别出水果的功能，识别成功率较高(This program uses MATLAB to realize the function of recognizing fruits in fruit pictures, and the recognition rate is higher.)

文本变图片源码例程,程序将文本转换到字节集数据，再取出每个字节数值进行计算后画到画板上。(After the text becomes image source routines , the program will convert the text to a set of bytes of data , and then remove each byte value is calculated to draw on the drawing board .)

基于信息熵的迭代法求最佳阈值，并附参考论文及KSW熵法求阈值的程序。两个程序经实验测试通过无误。(Entropy-based iterative method for optimal threshold, together with reference papers and KSW entropy threshold method procedures. Two experimental test procedures correctly.)

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.)

基于支持向量机方法的图像分割与目标分类.介绍了支持向量机用于车辆图像分类。以及用于单目标图像的分割和多目标图像的分割，介绍的较为详细。(Method of image segmentation based on support vector machine (SVM) and the target classification. This paper introduces the support vector machine (SVM) is used to vehicle image classification. And for single target image segmentation and target image segmentation, introduced detailed.)

用灰度模板匹配和灰度模板快速匹配两种算法实现了对两个图像的配准。(Matching two pictures with algorithms of modeling and fast modeling .)