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face recognition example for learning

说明：  双峰法阈值分割。阈值分割法是一种基于区域的图像分割技术，原理是把图像象素点分为若干类。图像阈值化分割是一种传统的最常用的图像分割方法，因其实现简单、计算量小、性能较稳定而成为图像分割中最基本和应用最广泛的分割技术。它特别适用于目标和背景占据不同灰度级范围的图像。它不仅可以极大的压缩数据量，而且也大大简化了分析和处理步骤，因此在很多情况下，是进行图像分析、特征提取与模式识别之前的必要的图像预处理过程。图像阈值化的目的是要按照灰度级，对像素集合进行一个划分，得到的每个子集形成一个与现实景物相对应的区域，各个区域内部具有一致的属性，而相邻区域不具有这种一致属性。这样的划分可以通过从灰度级出发选取一个或多个阈值来实现。(Threshold segmentation)

图像融合拼接GUI设计，包括图像融合和图像拼接的matlab实现。(Image fusion splicing GUI design, including image fusion and image stitching matlab implementation.)

Complete class-based image processing, contains all of the source code, auto image, There are cycle detection, periodic testing, MIT Artificial Intelligence Laboratory identification of the target source.

灰度双线性插值，缩放图像。可以对图像进行几乎无损的小倍数放大，倍数大时有损。(Gray bilinear interpolation, zooming the image. Can be almost non-destructive image to enlarge small multiples, multiples of large detrimental.)

说明：  最简单的进行两图像的加减和异或，同或，运算。(The simplest operation is to add and subtract two images, and to add and subtract two images.)

将一幅M×N的灰度图像用3×3平均滤波器进行模糊，分别再加上一定的高斯 噪声和均匀噪声。然后，设计一个维纳滤波器对这两幅图像进行复原，分别 计算这两幅图像复原前后的PSNR。(Gray-scale image of a MN 33 average filter is vague, respectively, plus a Gaussian noise and uniform noise. Then, design a Wiener filter to recover these two images, the two images before and after the recovery of PSNR were calculated.)

这个算法包实现了从多幅拥有共同结构的图像里提取出共同形状的骨架。( This package implements the algorithm for Sketching the Common as described in: Shai Bagon, Or Brostovsky, Meirav Galun and Michal Irani "Detecting and Sketching the Common", CVPR 2010)

图像融合的质量评价指标，每个文件都有详细说明，每个指标都有相应的论文出处。亲测，可以使用。(Image quality uation of fusion, each file has a detailed description of each indicator has a corresponding paper source. Pro-test, can be used.)

采用协方差的方式计算相关系数，分析图片的相似性，然后对图像进行匹配(The covariance of the way of calculating the correlation coefficient, and analyze the image similarity, and then to do image matching )