神经网络实现人脸识别任务

说明：  [Wang 2011]The Meaning of Interior Tomography

功能：应用Rudin-Osher-Fatemi模型进行图像去噪，在ROFtest.m 中输入图像，附带文档ROFscheme内容为此模型的数值分析过程，比较详细，适合初学者(Classic 􏰉 Rudin-Osher-Fatemi model denoising, the including documentation is the numerical analysis process of the model in detail, suitable for beginners)

MRF分割程序，C++编写，进行图像分割处理，帮助熟悉分割过程及能量最优求解(markov image segmentation, energy interation algorithm)

说明：  小波降噪，运用小波工具将图像进行分层处理，可以得到降噪后的图像，含有凯斯西储大学轴承数据(Wavelet de-noising: the image is layered by using wavelet tools, and the denoised image can be obtained)

凸多边形检测和显示--先把图像中的显著区域提取出来，然后用最小凸多边形囊括显著区域，并把凸多边形显示出来(Convex polygon detection and show- first images extracted significant area, and then use the smallest convex polygon covering a significant area, and the convex polygon displayed)

图像分割 画红线分割 实现图像矩阵的分块 非常简便(Draw the red line segmentation of image segmentation is very simple block matrix)

非线性贝叶斯过滤与应用估算和导航，非常不错的粒子滤波英文论文(Nonlinear Bayesian filtering with applications to estimation and navigation)

这个函数分配一个数据缓冲区与多个颜色指定系统上的乐队。这种类型的缓冲区允许表示彩色图像(例如,RGB)。 这个函数为每个指定分配缓冲区,一个二维表面颜色乐队。您可以使用MbufAlloc1d()和MbufAlloc2d()来创建单独的乐队一或二维 数据缓冲区,分别。 分配一个缓冲区后,我们建议您检查是否操作成功,使用MappGetError(),或通过验证,不是M_NULL返回的缓冲区的标识符。(This function allocates a data buffer with multiple color bands on the specified system. This type of buffer allows for the representation of color images (for example, RGB). This function allocates buffers that have a two-dimensional surface for each specified color band. You can use MbufAlloc1d() and MbufAlloc2d() to create single band one- or two-dimensional data buffers, respectively. After allocating a buffer, we recommend that you check if the operation was successful, using MappGetError(), or by verifying that the buffer identifier returned is not M_NULL.)

在激光三维测量中，提取每一张图片的激光条纹中心坐标得到激光中心线。(In the laser 3D measurement, the central coordinates of the laser stripe are extracted from each image to obtain the central line of the laser.)

关于EM算法的详细介绍，可以帮助初学者深入理解EM的来源及使用方法。(A detailed description on the EM algorithm that can help beginners in-depth understanding of the source and use of EM methods.)