人工智能模式识别经典EM算法的matlab编程应用

%EM-shift demo %a simple color-histogram tracking demo script % %Z.Zivkovic, B.Krose %An EM-like algorithm for color-histogram-based object tracking" %IEEE Conference CVPR, June, 2004 % %University of Amsterdam, The Netherlands %Author:Zoran Zivkovic, www.zoranz.net %Date: 17-5-2005 clear sAvi='C:\Local\PeopleDatasets\Caviar\inria\walk1.avi';%set your own avi file name nNImageStart=120;nNImageEnd=270;%select frames %choose the tracker %sTracker='MS' dScale=0.1; sTracker='EMS' beta=1.2; nIterationMax=6; %%%%%%%%%%%%%%%%%%% %Initialization %%%%%%%%%%%%%%%%%%% %manually select object data=aviread(sAvi,nNImageStart);im0=data.cdata; figure(1),imMask=roipoly(im0);%roipoly is from the Image processing toolbox %shape=>ellipse [ry,rx]=find(imMask);m0=mean([rx,ry])';V0=cov([rx,ry]); [K]=GaussKernel(V0);%make kernel of appropriate size %make object histogram data=GetAffineRegion(im0,m0,V0,K.rX,K.rY,K.sigmas); nBinsD=4;histO=ColorHist(data,nBinsD,K.rK); %state vector s0=MeanVarToS(m0,V0);nStates=length(s0);s=s0; %dynamic model s=A*s+W A=eye(nStates); W=[3^2 0 0 0 0; 0 3^2 0 0 0; 0 0 1^2 0 0; 0 0 0 1^2 0; 0 0 0 0 1^2]; lambda=0.2;%observation model p~exp(-0.5*(1-rho)/lambda^2) Pk=100^2*eye(nStates);%some high initial value for the variance %%%%%%%%%%%%%%%%%%% %Tracking %%%%%%%%%%%%%%%%%%% for iFrame=nNImageStart:nNImageEnd data=aviread(sAvi,iFrame);im0=data.cdata;%read an image %different trackers switch sTracker case 'EMS' %Z.Zivkovic, B.Krose %An EM-like algorithm for color-histogram-based object tracking" %IEEE Conference CVPR, June, 2004 %predict s=A*s; Pk=A*Pk*A'+W; %find max [xt,V]=SToMeanVar(s);%prediction as starting point for iIteration=1:nIterationMax [xt,V]=EMShift(im0,xt,V,histO,K,beta); end z=MeanVarToS(xt,V); R=FitGauss(im0,z,histO,K)*lambda^2; %Kalman filter KFgain=Pk*inv(Pk+R); s=s+KFgain*(z-s); Pk=(eye(nStates)-KFgain)*Pk; case 'MS' %D. Comaniciu, V. Ramesh, P. Meer %Kernel-Based Object Tracking, %IEEE Trans. Pattern Analysis Machine Intell., Vol. 25, No. 5, 564-575, 2003 %predict s=A*s; Pk=A*Pk*A'+W; %Find the local max using the prediction as the start point [xt,V]=SToMeanVar(s);%prediction as starting point for iIteration=1:nIterationMax xt=EMShift(im0,xt,V,histO,K,beta);%mean shift does not use the second moment end rho0=Similarity(im0,xt,V,histO,K); z0=MeanVarToS(xt,V); %measure V by trying a number of different scales rD=dScale*[0 0 0 0 0;0 0 s(3) 0 s(5); 0 0 -s(3) 0 -s(5)]'; %try bigger [xt,V]=SToMeanVar(s+rD(:,2)); for iIteration=1:nIterationMax xt=EMShift(im0,xt,V,histO,K,beta);%mean shift does not use the second moment end rho1=Similarity(im0,xt,V,histO,K); z1=MeanVarToS(xt,V); %try smaller [xt,V]=SToMeanVar(s+rD(:,3)); for iIteration=1:nIterationMax xt=EMShift(im0,xt,V,histO,K,beta);%mean shift does not use the second moment end rho2=Similarity(im0,xt,V,histO,K); z2=MeanVarToS(xt,V); %choose the best [dummy,ri]=max([rho0 rho1 rho2]); if (ri(1)==1) z=z0; end; if (ri(1)==2) z=z1; end; if (ri(1)==3) z=z2; end; R=FitGauss(im0,z,histO,K)*lambda^2; %since we can be far from a local minima for the V %the local curvature is not good estimate for the V uncertainty !!! %-use some fixed value - mean value from the EMS for example R(3,3)=1;R(4,4)=1;R(5,5)=1; %Kalman filter equations KFgain=Pk*inv(Pk+R); s=s+KFgain*(z-s); Pk=(eye(nStates)-KFgain)*Pk; end %show figure(1),imshow(im0);hold on;%imshow is from the Image processing toolbox h=PlotEllipse(s);set(h,'LineWidth',4,'Color',[1 0.7 1],'LineStyle','--'); drawnow;hold off end