GraduationImageProcessPlus

一个kalman滤波的仿真程序，仿真的是雷达探测飞机的飞行轨迹，里面的word文档说的比较详细了。(A kalman filter simulation procedures, simulation of the plane are radar flight path, inside the word document that has more detailed.)

说明：  用matlab绘制三次样条曲线。系数由c++程序计算得出。(Matlab draw with cubic spline curve. Coefficient by c++ Procedures calculated.)

贪婪算法 子载波分配 MATLAB代码 源代码(Greedy algorithm)

该算法基本思想是利用一组带有相应权值的随机样本即粒子去逼近PHD分布和基数分布，其意义在于不仅解决重积分计算没有闭式解的难题，而且在滤波过程中，PHD函数被一系列离散的带权值的样本近似，随着样本粒子数量的增加，PHDF接近于Bayes最优估计，而且不受模型线性和高斯假设的限制，可以适用于非线性非高斯的随机系统。(The algorithm the basic idea is to use a set of random sample with the corresponding weight namely particle approximation PHD and base distribution, its significance lies in not only solve the problem of multiple integral calculation without closed solution, and in the process of filtering, PHD function by a series of discrete weighted value of sample approximation, with the increase of sample particles, PHDF close to the Bayes optimal estimation, and is not subject to the assumptions of the linear and gaussian model can be applied to nonlinear non-gaussian stochastic system.)

说明：  蒙特卡洛法在MATLAB中的应用及其意义，并给出了EXCEL中的使用方法(Monte C Lofa's application in MATLAB and its significance, and gives the way of using in EXCEL.)

基于FFT谱连续细化算法的高精度频率估计，这种算法可以精确到小数点后两位，这种算法与MUSIC法和ESPRIT法相比，计算量大大降低，而且精度非常高。(This procedure is based on the FFT spectrum continual refinement algorithm s high accuracy frequency， this algorithm may precisely to decimal point latter two, compared the MUSIC algorithm and the ESPRIT algorithm, the computation complexity reduces greatly, moreover the precision is high.)

采用高斯核的多类分类,以wine数据位例，进行编程(the classification using gauss kernal)

This program is a Prime number generator. It generates prime numbers very fast. it uses many algorithms to speed up things and uses cache.

用编写的集机器人设计、分析、仿真与一体的软件，非常有工程使用价值，完全可用！(prepared with the set robot design, analysis, simulation and integration software, it works very useful and it is perfectly possible!)

The problem of image registration subsumes a number of problems and techniques in multiframe image analysis, including the computation of optic flow (general pixel-based motion), stereo correspondence, structure from motion, and feature tracking. We present a new registration algorithm based on spline representations of the displacement field which can be specialized to solve all of the above mentioned problems. In particular, we show how to compute local flow, global (parametric) flow, rigid flow resulting from camera egomotion, and multiframe versions of the above problems. Using a spline-based description of the flow removes the need for overlapping correlation windows, and produces an explicit measure of the correlation between adjacent flow estimates. We demonstrate our algorithm on multiframe image registration and the recovery of 3D projective scene geometry. We also provide results on a number of standard motion sequences.