化学化工的数学方法及matlab实现.pdf

【实例简介】Inverted_pendulum.slx

matlab匹配滤波器

多智能体最优一致性避障算法Matlab仿真源程序

【实例简介】

MATLAB GUI设计学习手记（第3版）源代码BY罗华飞

论文《As-Projective-As-Possible Image Stitching with Moving DLT》中的拼接算法，对于视差图像拼接具有一定的鲁棒性，但是对特征点数量及其分布均匀性有较高的要求。 The success of commercial image stitching tools often leads to the impression that image stitching is a “solved problem”.The reality, however, is that many tools give unconvincing results when the input photos violate fairly restrictive imaging assumptions;the main two being that the photos correspond to views that differ purely by rotation, or that the imaged scene is effectively planar.Such assumptions underpin the usage of 2D projective transforms or homographies to align photos. In the hands of the casual user,such conditions are often violated, yielding misalignment artifacts or “ghosting” in the results. Accordingly, many existing imagestitching tools depend critically on post-processing routines to conceal ghosting. In this paper, we propose a novel estimationtechnique called Moving Direct Linear Transformation (Moving DLT) that is able to tweak or fine-tune the projective warp toaccommodate the deviations of the input data from the idealized conditions. This produces as-projective-as-possible image alignmentthat significantly reduces ghosting without compromising the geometric realism of perspective image stitching. Our technique thuslessens the dependency on potentially expensive postprocessing algorithms. In addition, we describe how multipleas-projective-as-possible warps can be simultaneously refined via bundle adjustment to accurately align multiple images for largepanorama creation.

运用基于球径容积准则的CKF对惯性器件输出数据和GPS数据尽心融合处理，达到组合导航定位的目的

差分演化算法的matlab程序: function DE(Gm,F0)%差分进化算法程序基本程序%F是变异率   F0=0.6 Gm=1000; %最大迭代次数Np=100; %种群规模CR=0.9;  %杂交参数G=1;%初始化代数D=0;%所求问题的维数eps=1e-9;%精度ge=zeros(1,Np);%各代的最优值bestx=zeros(Np,D);%各代的最优解%产生初始种群%xmin=-10;xmax=100;%带负数的下界xmin=-5.12;xmax=5.12;%-----函数值计算-----------function result=DEMB(XX);     sol=XX;     result=0;for i=1:D    result=result sol(i)^2;    %y=sum(XX.^2-10.*cos(2.*pi.*XX) 10);endend%---------------------------X0=(xmax-xmin)*rand(Np,D) xmin;X=X0;%%%%%%%%%%变异操作X1new=zeros(Np,D);%初始化X1_new=zeros(Np,D);%初始化X1=zeros(Np,D);%初始化value=zeros(1,Np);while Gxmin)&(bonCR %利用二项分布来交叉            X1_new(i,:)=X(i,:);        else            X1_new(i,:)=X1new(i,:);        end    end %%%%%%%%%%%%%%%竞争操作    for i=1:Np     if DEMB(X1_new(i,:))

超分辨率 SRCNN

Matlab code of Bayesian CP Factorization for Tensor Completion(Written by Qibin Zhao 2014)To run the code:1. Change Matlab work directory to "/BCPF_Toolbox_QZhao/".2. Run  "loadpah" code to add the current folder and subfolders into Matlab path searching list.3. Open and run the demo files. We provide two demo codes:I.  DemoBayesCP.m:        Demonstration on synthesic dataII. DemoBayesCP_Image.m   Demonstration for image completionThe package includes four algorithms:1. BCPF.m           BCPF for fully observed tensor2. BCPF_TC.m        BCPF for incomplete tensor 3. BCPF_IC.m        BCPF for image completion4. BCPF_MP.m        BCPF using mixture priors for image completionIn this package, we used the tensor toolbox 2.5, which is downloaded from (http://www.sandia.gov/~tgkolda/TensorToolbox)The tools for visualization of tensor with voxels is from Tensorlab (http://www.tensorlab.net/)