dlt

基于相位相关的图像配准，图像的移动参数代码(Phase correlation-based image registration, image of the movement parameter code)

c源代码 图象的压缩编码，JPEG压缩编码标准 功能是行程编码，JPEG压缩编码（基本系统）(c code, function: jpeg stand for image show)

上传一张图片，这是在做图像水印研究时常用的一张lina图(image used in watermarking)

说明：  python语言，三种阈值分割方式，简单阈值分割，自适应阈值分割，OTsu分割方式(Python language, three methods of image segmentation , simple threshold segmentation, adaptive threshold segmentation, OTsu segmentation)

人脸检测程序。在菜单上有许多功能选项，可实现多角度，多目的检测。(face detection procedures. In the menu options are many functions that can be achieved from different angles, multi-purpose testing.)

说明：  求解在一个三角形上的任何一点，到另外一点的距离最小的一个点。(solution in a triangle on any point to another point of the smallest distance between a point.)

Cox 论文、程序matlab代码，仿真结果以及读书笔记，有助于学习(Cox papers, procedures matlab code, simulation results, as well as study notes, contribute to learning)

实现对输电线路导线舞动的实时检测。并且可对导线舞动的轨迹进行建模。(Galloping on the transmission line to achieve real-time detection. And conductor galloping track can be modeled.)

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.)

说明：  用数字图像处理方法提取书法特征 实现书法骨架提取(Using digital image processing method to extract calligraphy features)