| R. L. Kashyap and R. Chellappa. Estimation and Choice of Neighbors in Spacial Interaction Models of Images. IEEE Transactions on Information Theory, IT-29:60--72, January 1983. |
....[6] are used. We present efficient parallel algorithms to implement both methods. The MLE performs better than the LSE. This can be seen visually by comparing the textures synthesized from the LSE and MSE parameters, or by noting that the asymptotic variance of the MLE is lower than the LSE ( 3] [25]) 4.1 Least Squares Estimate of Parameters The least squares estimate detailed in [6] assumes that the observations of the GMRF image fy oe g obey the model y oe = r2Ns Theta r [y oe r y oe Gammar ] e oe ; 8oe ; 18) 17 where fe oe g is a zero mean correlated noise sequence with ....
R. L. Kashyap and R. Chellappa. Estimation and Choice of Neighbors in Spacial Interaction Models of Images. IEEE Transactions on Information Theory, IT-29:60--72, January 1983.
....[6] are used. We present efficient parallel algorithms to implement both methods. The MLE performs better than the LSE. This can be seen visually by comparing the textures synthesized from the LSE and MSE parameters, or by noting that the asymptotic variance of the MLE is lower than the LSE ( 3] [25]) 4.1 Least Squares Estimate of Parameters The least squares estimate detailed in [6] assumes that the observations of the GMRF image fy oe g obey the model y oe = X r2Ns Theta r [y oe r y oe Gammar ] e oe ; 8oe 2 Omega ; 18) where fe oe g is a zero mean correlated noise sequence ....
R. L. Kashyap and R. Chellappa. Estimation and Choice of Neighbors in Spacial Interaction Models of Images. IEEE Transactions on Information Theory, IT-29:60--72, January 1983.
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