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# Specify the size of <math>YY^T</math> and <math>Y^TY</math>. Which matrix is smaller? | # Specify the size of <math>YY^T</math> and <math>Y^TY</math>. Which matrix is smaller? | ||
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| + | <math>Y</math> is of size <math>p\times N</math>, so the size of <math>YY^T</math> is <math>p\times p</math>. | ||
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| + | <math>Y</math> is of size <math>p\times N</math>, so the size of <math>Y^TY</math> is <math>N\times N</math>. | ||
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| + | Obviously, the size of <math>Y^TY</math> is much smaller. | ||
# Prove that both <math>YY^T</math> and <math>Y^TY</math> are both symmetric and positive semi-definite matrices. | # Prove that both <math>YY^T</math> and <math>Y^TY</math> are both symmetric and positive semi-definite matrices. | ||
Revision as of 00:32, 7 July 2019
Communication, Networking, Signal and Image Processing (CS)
Question 5: Image Processing
August 2016 (Published on Jul 2019)
Problem 1
- Calculate an expression for $ \lambda_n^c $, the X-ray energy corrected for the dark current.
$ \lambda_n^c=\lambda_n^b-\lambda_n^d $
- Calculate an expression for $ G_n $, the X-ray attenuation due to the object's presence.
$ G_n=-\mu(x,y_0+n*\Delta d)\lambda_n $
- Calculate an expression for $ \hat{P}_n $, an estimate of the integral intensity in terms of $ \lambda_n $, $ \lambda_n^b $, and $ \lambda_b^d $.
$ \lambda_n=(\lambda_n^b-\lambda_n^d)e^{-\int_0^x \mu(t)dt} $
$ \hat{P}_n=\int_0^x \mu(t)dt=-log\frac{\lambda_n}{\lambda_n^b-\lambda_n^d} $
- For this part, assume that the object is of constant density with $ \mu(x,y)=\mu_0 $. Then sketch a plot of $ \hat{P}_n $ versus the object thickness, $ T_n $, in $ mm $, for the $ n^{th} $ detector. Label key features of the curve such as its slope and intersection.
Problem 2
- Specify the size of $ YY^T $ and $ Y^TY $. Which matrix is smaller?
$ Y $ is of size $ p\times N $, so the size of $ YY^T $ is $ p\times p $.
$ Y $ is of size $ p\times N $, so the size of $ Y^TY $ is $ N\times N $.
Obviously, the size of $ Y^TY $ is much smaller.
- Prove that both $ YY^T $ and $ Y^TY $ are both symmetric and positive semi-definite matrices.
- Derive expressions for $ V $ and $ \Sigma $ in terms of $ T $, and $ D $.
- Drive expressions for $ U $ in terms of $ Y $, $ T $, and $ D $.
- Derive expressions for $ E $ in terms of $ Y $, $ T $, and $ D $.
- If the columns of $ Y $ are images from a training database, then what name do we give to the columns of $ U $?
