QIM_DCT

1990年—adaptive multi-band CFAR detection of an optical pattern with unknown spectral distribution.pdf

Matlab程序，利用curvelet变换和高斯逆滤波进行图像去雾，含效果图(Matlab program, using curvelet transform and inverse Gaussian image filtering to fog, with the effect of map)

盲信号分离应用的图像盲分离的资料，给出了事例以及部分源码(Blind Signal Separation Application Blind Image Separation of information, and gives examples of some of the FOSS)

利用hough变换检测圆，有RHT，随机hough变换，以及一些改进(Using Hough transform to detect the circle)

多目标遗传算法，很好的源代码，实现多输入多输入参数条件下的最优化(Multi-objective genetic algorithm, a very good source code, achieve multiple-input multiple-input parameters optimized under the conditions of)

说明：  基于opencv平台编写的单目相机标定程序，使用的张正友的方法，只要准备十二张单目相机拍摄的棋盘格的图片就可以标定出单目相机的焦距等参数，建立单目相机坐标系。可以用于机器视觉测量中，也可以用于双面相机标定的前一步，精度很高。(Based on the monocular camera calibration program compiled by the OpenCV platform, the method of Zhang Zhengyou is used to calibrate the focal length and other parameters of the monocular camera and establish a monocular camera coordinate system as long as 12 pieces of chessboard pictures taken by the monocular camera are prepared. It can be used in machine vision measurement, and it can also be used in the previous step of the calibration of the double-sided camera, with high precision.)

医学图像大部分都是dicom图像，这里提供dicom图像读取、显示和保存的matlab代码，以为医学图像处理初学者提供学习。 (Dicom medical images are mostly images, here to read dicom image, display, and save the matlab code, provided that the medical image processing for beginners to learn.)

完整正确的仿射变换程序，测试过绝对没有错(Complete the correct affine transformation procedures, the test had absolutely nothing wrong with)

本人用matlab自己做的图像分裂合并程序，主要对印章图像进行处理．(own image split the merger process, mainly to seal image processing.)

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)