Impulse_Response

佳能数码相机图像处理源码工具(EOS 350D, 20D, 1D等)(Canon digital camera image processing source tools (EOS 350D, 20D, 1D, etc.))

基于HOUGH变换的车道线检测和识别的程序，可以使用(Lane HOUGH transform detection and recognition based on the program, you can use)

        本例子集成了“MLP”,"SVM","KNN","GMM",分类器算法，可以作为进行物体分类，缺陷检测的参考案例。

立体匹配中的单目标定,输入图片，得出结果，官方实例(Monocular calibration)

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)

说明：  使用sift进行的图像拼接，matlab语言实现(Image mosaic using sift)

huffman编码 基于对BMP格式图像的编码！ 具体你们自己看看！(Huffman code)

说明：  分类，纹理特征，提取特征向量，matlab 7.0 原程序，classifer,texture(Classification, texture features, feature vector extraction, matlab 7.0 original procedure, classifer, texture)

【实例简介】

打开BMP图像 IHS Brovey图像融合(Open the BMP image IHS Brovey Image Fusion)