Hi
I'm looking for a matlab code which estimates the two principal directions (axes) using principal components analysis (PCA)?? Then any an other algorithm can be applied to estimate the asymmetry of the region of image in terms of the two principal axis. I tried several times to implement it and I looked everywhere to no avail. Now I ask my question on this forum hoping to get the right answer. thanks
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Refer below link:
http://matlabdatamining.blogspot.jp/2010/02/principal-components-analysis.html
Hope it will be useful.
I tried to do this code but i need your help please
I2=segmentation(I); [n m] = size(I2); AMean = mean(I2); co=I2*I2'; % Compute the covariance matrix (co) % Compute the eigen values and eigen vectors of the covariance matrix [eigvector,eigvl]=eig(co); eigvalue = diag(eigvl); pc = eigvector * I2; plot(pc(1,:),pc(2,:))
Hi
I'm looking for a matlab code which estimates the two principal directions (axes) using principal components analysis (PCA)?? Then any an other algorithm can be applied to estimate the asymmetry of the region of interest of the segmented image in terms of the two principal axis. I tried to do this code but it doesn't work so i need your help.
Hi, Can any one please correct this code
A=imread('aeroplane silhouette.png');
bw=~im2bw(A,0.5); %Threshold and invert
subplot(1,2,1), imshow(bw,[]);
[y,x]=find(bw>0.5); %Get coordinates of non zero pixels
centroid=mean([x y]); %Get (centroid) of data
hold on; plot(centroid(1),centroid(2),'rd'); %Plot shape centroid
C=cov([x y]); %Calculate covariance of coordinates
[U,S]=eig(C)
m=U(2,1)./U(1,1);
const=centroid(2)/m.*centroid(1);
xl=50:450; yl=m.*xl+const
subplot(1,2,2), imshow(bw,[]); h=line(xl,yl); %Display image and axes
set(h,'Color',[1 0 0],'LineWidth',2.0)
m2=U(2,2)./U(1,2);
const=centroid(2)/m2.*centroid(1);
x2=50:450; y2=m2.*x2+const
h=line(x2,y2);
set(h,'Color',[1 0 0], 'LineWidth',2.0)
you find the images here
http://img15.hostingpics.net/pics/679738aeroplanesilhouette.png
Before asking us to correct the code, you should tell us what's wrong with the code. From the second image, it seems that the gray lines lie along what I would expect to be the principal components.
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