The detector based on the MAP criterion chooses the most
likely CI among all possible CIs, given the observed images.
The resulting probability of error is minimum among all other
detectors [12], [13]. The structure of the MAP detector is based
on statistical models. Therefore, the accuracy of the statistical
models for the given images as well as for the CI is crucial. In
this paper, we assume that the given images are obtained by the
summation of noiseless image models (NIMs) and image noises.
Both the NIMs and the CI are assumed to have MRF properties.
Furthermore, we assume that configurations of the NIMs
are equal for unchanged sites. In addition, the configurations of
changed sites from one NIM are independent of configurations
of changed sites from the other NIM when the configurations
of unchanged sites are given. Then, the a posteriori probability
is determined, based on the above assumptions, and the MAP
criterion is used to select the optimum CI.
The detector based on the MAP criterion chooses the mostlikely CI among all possible CIs, given the observed images.The resulting probability of error is minimum among all otherdetectors [12], [13]. The structure of the MAP detector is basedon statistical models. Therefore, the accuracy of the statisticalmodels for the given images as well as for the CI is crucial. Inthis paper, we assume that the given images are obtained by thesummation of noiseless image models (NIMs) and image noises.Both the NIMs and the CI are assumed to have MRF properties.Furthermore, we assume that configurations of the NIMsare equal for unchanged sites. In addition, the configurations ofchanged sites from one NIM are independent of configurationsof changed sites from the other NIM when the configurationsof unchanged sites are given. Then, the a posteriori probabilityis determined, based on the above assumptions, and the MAPcriterion is used to select the optimum CI.
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