daqituanliu

说明：  李春明于2005年提出的CLIC，一种对MR图像偏移场矫正方法(Li Chunming's CLIC in 2005, an offset correction method for MR images.)

opencv实现对图像的旋转、平移、缩放(opencv rotate move zoom)

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.)

包括影像预处理，叶绿素反演，水污染监测等功能(Including image preprocessing, water pollution monitoring and other functions)

图形学的有一个作业(graphics operations of a)

二维最小均方误差背景预测算法TDLMS，常用于目标检测，经实测，效果较好(Two dimensional minimum mean square error background prediction algorithm TDLMS, commonly used in target detection, measured, the effect is better)

使用4阶龙格库塔定理计算一阶微分方程组，方程组有3个方程(RK calculation of differential equations)

基于灰度共生矩阵的图像检索技术。完整的特征提取、特征描述、特征匹配以及结果返回。(GLCM-based image retrieval techniques. A complete feature extraction, feature description, feature matching and the result returned.)

目前电子稳像技术应用最为广泛的算法，包括特征点的提取和运动补偿。(Electronic image stabilization technology currently the most widely used algorithms, including the feature point extraction and motion compensation.)

运用pca算法降维，提取主要特征值，从而达到降维目的(Dimensionality reduction using pca algorithm, extract the main features of the value of)