the-shortest-route

显示关键点，返回值为关键点的坐标，尺度，方向，模值，描述符(return the (x,y),scale,derection,size)

Eigenfaces: PCA tends to find a p-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space (p  N). We called the new subspace defined by basis vectors “face space”. First, all training faces are projected onto the face space to find a set of weights that describes the contribution of each vector. Then we project all testing faces onto the face space to obtain a set of weights. Finally, we identify the face by comparing a set of weights for the testing face to sets of weights of training faces.

视频运动估计中快速块匹配方法　　　菱形搜索(The video motion estimation based on fast block matching Diamond　Search)

分别用Ansys和Matlab建立的混凝土骨料随机投放模型，建立二维，三维数值混凝土模型，对分析混凝土的破坏机理具有重要参考价值。(Concrete aggregates were used Ansys and Matlab established random delivery model, a two-dimensional, three-dimensional numerical model of concrete, concrete analysis of the failure mechanism has important reference value.)

ct投影重建（shep-logan模型的生成投影以及重建）(ct projection reconstruction (shep-logan model for the generation of projection and reconstruction))

说明：  信道仿真,小尺度衰落信道的MATLAB仿真源码(matlab jakes)

MATLAB SIMULINK建模与仿真实例精讲。(MATLAB SIMULINK earnestly modeling and simulation examples.)

基于matlab平台实现,基于互功率谱的时延估计，含噪脉冲信号进行相关检测。( Based on matlab platform, Based on the time delay estimation of power spectrum, Noisy pulse correlation detection signal.)

完成复杂网络基本操作，基于网络图模型，可与图论算法及其matla实现配套使用(Complete complex network basic operation, based on network graph model)

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle s position and velocity. Each particle s movement is influenced by its local best known position and is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.