Adaboost人脸识别

带通配符的字符串匹配,多重匹配,支持通配符“*”，“？”(String matching with wildcards, multiple matches, supports wildcards " *" , " ?" )

将图像从笛卡尔坐标系上转换到对数极坐标上，这样做便于将笛卡尔坐标系上存在缩放和旋转关系的图像转换成对数极坐标下的具有平移关系的图像，然后就可以用相位相关检测算法进行相关检测，得到两幅图像的相关性(Convert an image Cartesian to polar coordinates on a logarithmic, this facilitates the translation relations image with the next image conversion to a logarithmic scale and rotate relationship exists between the Cartesian coordinates polar coordinates, then you can use phase correlation detection algorithm for correlation detection, the correlation between two images obtained)

一个很好的学习图像处理的函数的例子程序，对于初级学习者来说，有很好的帮助，对于初学习者非常适合！(a good learning image processing function examples procedures for primary learners, good help, very suitable for early learners!)

ITK的脑室图像分割,可以对脑部的图像用itk进行分割，用VTK来进行显示。(the segration of brain of ITK )

机器人的轨迹跟踪程序，修改参数可以重新建立自己想要的模型，可以看到末端轨迹的运动画面。(Robot trajectory tracking program, you can modify the parameters to re-establish the model you want, you can see the end of the trajectory of the moving picture.)

本程序实现图像LENA的压缩传感 程序作者：沙威，香港大学电气电子工程学系，wsha@eee.hku.hk(The procedure of image compression sensing process by LENA: Javert, University of Hong Kong Electrical and Electronic Engineering, wsha@eee.hku.hk)

实现图像配准:线性保角变换，投影变换及仿射变换。(Achieve image registration: linear conformal transformation, projection transformation and affine transformation.)

meanshift 分割算法meanShift,均值漂移，在聚类、图像平滑、分割、跟踪等方面有着广泛的应用。meanShift这个概念最早是由Fukunage在1975年提出的，其最初的含义正如其名：偏移的均值向量；但随着理论的发展，meanShift的含义已经发生了很多变化。如今，我们说的meanShift算法，一般是指一个迭代的步骤，即先算出当前点的偏移均值，然后以此为新的起始点，继续移动，直到满足一定的结束条件。(meanshift Segmentation algorithm)

利用位图图像中的冗余数据位，将小文件嵌入到大的图像文件中，并保持原图像不变。(The use of bitmap images in the redundant data bit will be embedded in small files to large image files, and to maintain the original image unchanged.)

这是本人找到的非常好的几何特征提取源代码，包括周长。面积。伸长度、矩形度等用过了，很不错的！(This is my very best to find the source code for geometric feature extraction, including the perimeter. Area. Elongation, rectangular degree used, very good!)