sar影像小波去噪

在文档下打开一个位图文件，重新绘制，放大并使其二值化显示，()

图像拼接小程序，使用直接对比的方法进行拼接并用加权平均的方法进行融合 (a simple program for image splicing )

对两幅图像进行配准，分别提取两幅图像的surf特征点以及描述子，得到粗匹配结果，然后根据粗匹配结果，采用ransac方法计算基础矩阵，并去除误匹配点，得到较准确匹配结果(Two image registration, surf was extracted from the feature points in two images to get the coarse matching and descriptor, then according to the results, the coarse matching results, using RANSAC method to calculate the fundamental matrix, and eliminate the error matching, gain a more accurate matching results)

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.)

Opencv处理图像，用ostu法实现图像的二值化(Opencv image processing, using ostu method to achieve image binarization)

运用在fluent中的udf，解决边界正弦运动的情况，非常珍贵(Border harmonic vibration UDF, use them in fluent, very precious resource)

主要用于打开8位、24位BMP图像，并对其进行相应的处理，如：颜色分量提取，检测等(key to open the eight, 24 BMP images, and its corresponding treatment, such as : color component extraction, testing)

利用OpenCV实现单幅图片对于手指的精确识别(Single image using OpenCV to achieve precise identification of the finger)

利用棋盘格对相机进行标定，加入误差判定及像素空间到实际物理空间的距离计算(The camera is calibrated by checkerboard, the error is added and the distance of pixel space to the actual physical space is calculated)

最经典之做，保证别人没有上传过 实现文章 “GrabCut" Interactive Foreground Extraction using Iterated Graph Cuts 用graphcut实现图像分割，效果非常好(The most classic to do to ensure that other people do not realize uploaded article GrabCut Interactive Foreground Extraction using Iterated Graph Cuts with graphcut realize image segmentation, has very good results)