target-tracking

Retinex理论的基础理论是物体的颜色是由物体对长波（红色）、中波（绿色）、短波（蓝色）光线的反射能力来决定的，而不是由反射光强度的绝对值来决定的，物体的色彩不受光照非均匀性的影响，具有一致性，即retinex是以色感一致性（颜色恒常性）为基础的。不同于传统的线性、非线性的只能增强图像某一类特征的方法，Retinex可以在动态范围压缩、边缘增强和颜色恒常三个方面打到平衡，因此可以对各种不同类型的图像进行自适应的增强。(The basic theory of Retinex theory is the color of the object by object for the long wavelength (red), medium (green), short wave (blue) light reflection ability to decide, rather than by the intensity of reflected light of the absolute value to decide, the color of the object under illumination nonuniformity effects, consistent with the Retinex, that is based on color constancy (color constancy based). Different from the traditional linear, nonlinear image enhancement method of only one kind of features, Retinex can be in dynamic range compression, edge enhancement and three aspects of color constancy hit the balance, thus enhancing adaptive to a variety of different types of images can be.)

Canny边缘检测算法,已经试过，可以用。能较好的检测边缘。(Canny edge detection algorithm, has been tried, you can use. To better edge detection.)

CT（computer tomography ）图像重建算法，FBP，是目前商用CT的主流重建算法(computer tomography reconstruction algrithm)

相机的单目标定，使用张正友标定法，可靠使用(The monocular camera calibration, using the zhangzhengyou calibration method, reliable use)

Tsai标定算法，用用于图像标定的，可以使用 (Tsai calibration algorithm used for image calibration, you can use)

sift角点检测及匹配，在不同尺度空间下的角点检测方法，很方便实用。(sift corner detection and matching, the corner detection methods under different scales of space, it is convenient and practical.)

说明：  该程序实现的功能是：图像反色，线形变换，对于图像处理初学者非常实用。(The realization of the program)

EM算法以及混合高斯模型，c++实现，控制台程序，函数调用很简单方便。可以在低版本vc6.0运行。(EM algorithm and hybrid Gauss model, c++ implementation, the console program, function call is very simple and convenient. Can be run at low vc6.0.)

基于meanshift算法的目标跟踪，主要是基于opencv车辆跟踪！！！程序当中进行简单的图像分割(Based on the meanshift algorithm of image segmentation and target tracking.)

改程序就代码可以找出从不同角度或区间的两幅遥感影像的同名点(Change the program code can find it from different angles of the same name point or interval of two remote sensing images)