Grawing

用来识别火焰边缘，对高速摄影拍摄的火焰图片边缘进行提取(To identify the edge of a flame)

考虑雨衰 阴影 和多径影响，可以广泛的应用于数据预测及数据分析,采用了小波去噪的思想。( Consider shadow rain attenuation and multipath effects Can be widely used in data analysis and forecast data, Using wavelet denoising thought.)

海思图像调节方法和工具，很基础同时也很重要。(This document instructs debugging personnel to adjust the picture quality and implement differentiated adjustment.)

使用echat 绘制的地图 百度地图api(Map drawn using echat baidu map api)

边缘检测的各种经典算法，很值得一看。机会不容错过。快来看呀。(edge detection algorithm for the classic, is an eye-catcher. The opportunity should not be missed. Come see ah.)

说明：  图片的平滑锐化以及二值化。。。。。。。。。。。。。。。(Sharpening and smoothing the image binarization)

使用帧差法、均值法和混合高斯模型对背景进行去除、提取前景信息。来源于2017年研究生数学建模比赛D题。(The background information is removed and the foreground information is extracted by frame difference method, mean value method and hybrid Gauss model. D graduate from the 2017 mathematical modeling contest.)

VC++实现数字水印技术，在BMP位图中添加各种水印信息，程序分LSB和DWT嵌入方法。(VC++ implementation of digital watermarking technology, adding various watermarks in BMP bitmap, the program points LSB and DWT embedding methods.)

标记分水岭的matlab程序。分水岭变换就是指将原图像转换为一个标记图像, 其中所有属于同一集水盆的点均被赋予同一个标记, 并用一个特殊的标记来标识分水岭上的点(Marking a watershed in the matlab program. Watershed transform refers to the original image is converted to a Tagged Image, which all belong to the same set of points were given a basin with a marker, and a special marker to identify the point of a watershed)

基于Fisher线性判别基础学习器的集成分类器专门用于数字媒体的隐写分析，目前使用高维特征空间。目前，由于其良好的检测精度和较小的计算成本，它可能是设计监督分类器用于数字图像隐写分析的最常用方法。(The ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble.)