USPSdata

zcz cdma system for matlab(zcz cdma system)

能实现最小包围盒法方格划分，并利用中值进行点云精简(The minimum bounding box method can be implemented and the median value can be used to simplify the point cloud.)

KPCA是一种PCA的改进方法,很值得一看,很实用!(KPCA is an improved method of PCA, it is worth a visit, it is useful!)

车牌矫正,对于一写不够标准的车牌进行矫正，让车牌更容易被分割(Plate correction, not enough for a standard license plate write to correct, so more likely to be split plate)

自适应阈值算法将前景从背景中分离出来包括非均匀光照。(Adaptive threshold algorithm to the prospect of separating from the background, including non-uniform illumination.)

说明：  批量提取遥感影像数据，以如何在一个文件批量读取geotif数据为例(Batch extraction of remote sensing image data)

5.在面板中画圆弧的程序，帮助你画出你想要的圆弧。(5 arc in the panel painting program to help you draw your desired arc.)

说明：  能够检测图片的特征点，利用DOG算子，实际测试方便，算法清晰，有相关参考文章链接(Be able to detect image feature points)

PCB板检测的大概流程如下：首先存储一个标准PCB板图像作为参考标准，然后将待检测的PCB板图像进行处理，比较与标准PCB图像的差异。根据差异的情况来判断缺陷类型。(The approximate process of PCB board detection is as follows: first, a standard PCB board image is stored as a reference standard, and then the PCB board image to be detected is processed to compare the difference between the standard PCB image and the standard image. The type of defect is judged according to the difference.)

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.)