The use of PCA on a 64x64 Matrix
- From: oliverjb@xxxxxxxxx
- Date: 23 Dec 2005 18:03:09 -0800
Hi,
I'm new to image processing, and am having trouble fitting my head
around some topics, this algorithm in particular.
I have an image, which has been converted into a 64x64 matrix (each
value being the brightness of the pixel at that spot).
For the principal component analysis, I have first totted up the values
of all the pixels, and then divided by 64 * 64, giving me the mean
value of the pixels. I then took thbis value away from the values of
all the pixels.
Is this right? Much of the literature I read referred to N column
vectors, and finding the mean for each column vector. In my case this
would mean I had 64 vectors.
My next step was to calculate the Covariance matrix, by C = M *
M(transposed). This was easy enough. I then calculated the eigenvectors
using JAMA.
However, my main problem is that I have a 64 * 64 matrix of
eigenvectors. My aim was to have 2d eigenvectors, so I could find the
principal component, and then from this, theta.
Can anyone correct me where I'm going wrong? Many thanks.
.
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