jixielingjianshibie

卡尔曼滤波的一个典型实例是从一组有限的，包含噪声的，对物体位置的观察序列（可能有偏差）预测出物体的位置的坐标及速度。在很多工程应用(如雷达、计算机视觉)中都可以找到它的身影。同时，卡尔曼滤波也是控制理论以及控制系统工程中的一个重要课题。(A typical example of the Kalman filter, from a limited set, containing the noise, the position of the object observed sequence (there may be deviation) to predict the coordinates of the location and speed of the object. Its presence can be found in many engineering applications (such as radar, computer vision). Meanwhile, the Kalman filter is control theory, and control is an important topic in systems engineering.)

图片压缩到最小源码，例程程序结合易语言图像格式转换支持库，将快照的BMP图片转换成JPG图片，再将众多JPG图片压缩到一个压缩文件中，达到减小图片体积的效果。(Image compression to a minimum source , routine program combined image format conversion easy language support library , a snapshot of BMP images into JPG image , and then a large number of JPG images into a compressed file , to reduce the effect of image volume .)

SIFT特征提取 http://www.opencvchina.com/forum.php?mod=viewthread&tid=209&fromuid=267 (SIFT feature extraction http://www.opencvchina.com/forum.php?mod=viewthread& tid=209& fromuid=267)

SAR图像理解与解译研究进展,电子学报,(SAR image understanding and interpretation of research)

说明：  噪声估计法，先定位噪声点，再实行去噪处理(Noise estimation method, first locate the noise points, then implement denoising.)

可以快速准确的找出光斑、或者任何椭圆形状二值图像的质心(The center of mass of a two value image can be quickly and accurately identified.)

合成孔径雷达（SAR）成像的距离徙动校正(Synthetic aperture radar (SAR) imaging distance migration correction)

利用小波变换融合红外图像与可见光图像，提高图像质量(Infrared image and visible light image are fused by wavelet transform to improve image quality)

三维激光点云配准是点云三维建模的关键问题之一。经典的 ICP 算法对点云初始位置要求较高且配准 效率较低，提出了一种改进的 ICP 点云配准算法。该算法首先利用主成分分析法实现点云的初始配准，获得较好 的点云初始位置，然后在经典 ICP 算法的基础上，采用 k － d tree 结构实现加速搜索，并利用方向向量夹角阈值去除 错误点对，提高算法的效率。实验表明，本算法流程在保证配准精度的前提下，显著提高了配准效率。 (Three-dimensional laser point cloud registration is one of the key three-dimensional point cloud model. High classical ICP algorithm to the initial position of the point cloud registration requirements and low efficiency, proposed an improved ICP point cloud registration algorithm. Firstly, the use of principal component analysis of the initial point cloud registration, get a better initial position of the point cloud, then the basis of classical ICP algorithm using k- d tree structure to achieve speed up the search, and using the direction vector angle the removal of the threshold point error and improve the efficiency of the algorithm. Experiments show that the algorithm processes to ensure the accuracy of registration under the premise, significantly improve the efficiency of registration.)

说明：  本rar文件是图像超分辨率重构的网络模型RCAN的代码，可以成功运行(This rar file is the code of the image super-resolution reconstruction network model RCAN, which can be run successfully)