matlab

有详实的说明，基于C-V模型的图像分割，属于活动轮廓模型，有典型的图片例子，可以直接运行，适合研究生使用。(There are detailed instructions, CV model-based image segmentation, are active contour models, with typical picture examples can be run directly, for graduate students to use.)

说明：  用光流法估计图像的运动，基于像素点的算法(Light flow method estimates the image campaign, pixel-based algorithm for point)

1.6尺度不变特征提取，内附代码和文件说明，实证可用(1.6 Scale invariant feature extraction, enclosed code and document description, the empirical results are available.)

基于sift算法的图像匹配。并用RANSEC算法去除错误匹配。最后还给出了sift算法的图像拼接。(Sift-based image matching algorithm. And use RANSEC algorithm to remove false matches. Finally, given the sift algorithm for image stitching.)

gmcalab fast generalized form component analysis, Multi-target tracking particle filter, Calculate the multifractal trend fluctuation analysis.

Speedy GA Graphical analysis

纯MATLAB代码，不需要在使用SIFTWIN32可以直接运行的代码(100 code of MATLAB,u neednot SIFTWIN32 desinge the map)

说明：  进行三维去高后，根据sar参数信息，将斜距转变为地距。(After performing three-dimensional height removal, the slope distance is converted to ground distance according to the sar parameter information.)

针对杂波环境下的多个机动目标跟踪问题, 本文将多模型概率假设密度 (Multiple-model probability hypothesis density, MM-PHD) 滤波器和平滑算法相结合, 提出了 MM-PHD 前向 – 后向平滑器. 为了避免引入复杂的随机有限集 (Random finite set, RFS) 理论, 本文根据 PHD 的物理空间 (Physical space) 描述法推导得到了 MM-PHD 平滑器的后向更 新公式. 由于 MM-PHD 前向–后向平滑器的递推公式中包含有多个积分(By integrating the multiple-model probability hypothesis density (MM-PHD) filter with the smoothing al- gorithms, an MM-PHD forward-backward smoother is proposed in this paper for tracking multiple maneuvering targets in clutter. To avoid use of complex random finite set (RFS) theory, the backward updated equation of the MM-PHD smoother can be derived according to the physical-space explanation of the PHD)

一款新的数据分析工具，非常简单易用，用在系统辩识中(A new data analysis tools, very easy to use, used in System Identification)