MOIRE

用于各种特征提取主成分分析法，如表情提取，人脸提取，识别分类等(For a variety of features to extract the principal component analysis (pca)，Such as expression extraction, face extraction, identification classification, etc)

用于图象的复原处理，可以对多张同样角度拍摄的图片进行分析，得到清晰的放大图． superresulution(Rehabilitation treatment for the image can be more than the same angle picture taken for analysis have been given clear Enlargement. superresulution)

说明：  标注节点距离，批量标注节点距离。节省画图时间 提高画图效率(Marking Node Distance)

图像识别算法，使用点阵方式识别图片，借用Tessnet2.zip 组件，用来进行图片文字/图片数字识别(Tessnet2.zip use to be Image recognise)

说明：  对三角网格进行驱动形变，使用Deformation transfer of Triangular Mesh算法(Driving Deformation of Triangular Mesh)

关于边缘检测、图像分割matlab源程序，Prewitt算子、LoG算子检测、阈值分割、水线阈值法分割、文字和非文字区分等(on edge detection, image segmentation Matlab source, Prewitt operator, Getting Operator detection, thresholding segmentation, subaqueous threshold method segmentation, textual and non-textual distinction)

说明：  本程序利用为自适应中值滤波器的实现，可用于图像去噪(An image noise reduction algorithm)

SVT 低秩矩阵恢复，可用于稀疏表示、图像恢复(SVT for low rank matrix completion)

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.)

线性反投影算法（Linear Back Projection，简称 LBP）又称累加法，是最早使用的一种简单（ECT图像重建）成像算法。它将通过某点的所有投影射线进行累加，再反向估算出该点的密度值。从成像观点分析，它是不完全的雷登逆变换（完整的雷登逆变换包括微分、希尔伯特变换、反投影和归一化等步骤）。(Linear back projection algorithm (Linear Back Projection, referred LBP), also known as cumulative method, is the first to use a simple (ECT image reconstruction) imaging algorithms. It accumulates all projection rays through a point, then reverse the estimated density value of the point. From the image point of view analysis, it is not exactly the inverse transform Leiden (Leyden complete inverse transformation involving differential Hilbert transform backprojection and normalization steps, etc.).)