thresholddenoise

数字水印实现数字水印实现数字水印实现数字水印实现(The digital watermark digital watermark digital watermark to achieve)

主动轮廓模型结合一个特殊的显著性方法实现目标分割(using active contour with a special salient detction modle to segment an image )

在IDL中运行，可以将多光谱、高光谱等多波段影像拆分为为单一波段影像(It works in IDL and it can split multibands into single band)

基于图像灰度共生矩阵的特征提取代码，程序实现0 45 90 135度4个方向的特征提取(GLCM based on image feature extraction code, the program achieved 04,590,135 degrees in four directions of the feature extraction)

可利用哈夫变换法在变换域提取直线，利用g共线和直线相交的关系，使直线提取问题转化为计数问题()

实现基于混合高斯模型的运动目标检测算法的仿真(Achieved based on Gaussian mixture model simulation of moving target detection algorithm)

读取RAW格式文件，将其转换为BMP格式文件并存储，主要实现灰度图像的转换(Read the RAW format, convert it to BMP format file and stored, mainly to achieve the conversion of grayscale images)

一些常见的工程光学现象的模拟，如光的衍射，光的全息(Some common optical phenomena simulation projects, such as light diffraction, optical holographic)

检测道路边缘，利用道路的平行线特性，来检测道路所在直线。(Detect edges of the road, the road parallel to the line features to detect the road where the straight line.)

混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。(Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of)