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I suspect you are aiming at what is known as eigenimage or eigenface analysis. A key step resides in computing the eigenvectors, often performing with preprocessing (e.g. mean subtraction) followed by vectorizing 2D images (aligning pixels in a 1D vector), before concatenating vectors and computing the eignevectors, with fast alternatives.


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A very simple example on a $2\times 2$ image $$I_0=\begin{bmatrix}a&b\\c&d\end{bmatrix}$$ with (very crude Gaussian) low-pass: $$g=1/4\begin{bmatrix}1&1\\1&1\end{bmatrix}$$ yields a downsampled $I_1$ after filtering, with only one pixel (out of 4): $$I_1=\begin{bmatrix}(a+b+c+d/4)\end{bmatrix}$$ It can be upsampled as: $$U(I_1) = I_1^\...


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Actually the down sampling has no role here. It is all based on a real simple equation: $$ I = A + B $$ It is always enough to keep 2 terms of the 3 to restore completely and perfectly the information. So let's look on this: $$ {I}_{0} = \left( \left( {I}_{0} \downarrow \right) \uparrow \right) + {R}_{0} $$ So if we keep $ {R}_{0} $ and we have $ \left( ...


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Because wo want to get the centroid of the image(a block/patch) by the intensity. m00:p = q = 0,sum the intensity matrix. m10:p =1,q = 0,sum of the x-direction. m01:p = 0,q = 1,sum of the y-direction. (m10/m00,m01/m00) is the centroid.


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