12148086KSVD_Matlab_ToolBox

Pyramid methods in image processing

本案例采用基于块匹配的图像拼接流程来执行拼接操作，载入图片文件夹来作为待拼接对象，通过进行图片序列的匹配、融合来得到拼接效果，并分别对灰度图像、彩色图像进行处理。(This case is based on block matching process to perform image stitching splicing operation, load images folder as an object to be spliced, by matching the fusion image sequence to get the mosaic effect, respectively, on the gray image, color image processing .)

新的形态学图像增强，采用多尺度形态学增强算法，对图像进行增强(morphology image enhance )

形态学pathopenings的实现，对线特征提取等图像处理有令人惊奇的效果。(Morphological pathopenings the realization of the line feature extraction and other image processing have surprising effects.)

基于contourlet的图像融合，低频分量在融合过程中使用加权平均的融合规则(Contourlet-based image fusion, the low frequency components in the integration process using the weighted average of the fusion rules)

MD5 algorithm can be directly called debugging no problem not subject to environmental restrictions

快速k-means算法,比matlab 自带的要快很多,不错的源码(K- means algorithm quickly, much faster than with matlab, good source)

说明：  图像分割程序，可以分割红外车辆目标、人物等目标和图像。(Image segmentation program, you can split infrared target vehicles, characters and other objectives and image.)

SAR图像分类程序，基于灰度阈值的分类，无监督学习的分类简单。(SAR Image classification)

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)