A Markov random field (MRF) model-based EM (expectation-maximization) procedure for simultaneously estimating the degradation model and restoring the image is described. The MRF is a coupled one which provides continuity (inside regions of smooth gray tones) and discontinuity (at region boundaries) constraints for the restoration problem which is, in general, ill posed. The computational difficulty associated with the EM procedure for MRFs is resolved by using the mean field theory from statistical mechanics. An orthonormal blur decomposition is used to reduce the chances of undesirable locally optimal estimates. Experimental results on synthetic and real-world images show that this approach provides good blur estimates and restored images. The restored images are comparable to those obtained by a Wiener filter in mean-square error, but are most visually pleasing.
In this paper, we describe a novel approach for image sequence segmentation. It contains three parts: global motion compensation, robust frame differencing, and curve evolution. It is computationally efficient, does not require dense-field motion estimation, and is insensitive to noise and global/background motion. It works for black-and-white and color image sequences. The efficacy of this approach is demonstrated on both TV and surveillance image sequences.
A multiresolution statistical model, consisting of random fields in wavelet subbands, is proposed for texture, and has produced promising results in texture synthesis experiments. The basic idea here is that a complex random field model, e.g., one that contains long-range and nonlinear spatial correlations, can be constructed from several simpler ones.
A recently developed new paradigm for probabilistic robustness analysis does not require apriori information about the underlying distribution function for the uncertain parameters; only ai mild monotonicity and symme try assumption is involilred. The starting point is exactly the same as in classical robustness theory -a system with uncertain parameters which are only known within given bounds. However, instead of calculating the classical robustness margin for such a system, a risk-adjusted margin is sought. The theory suggests that the "best" way to sample the uncertain parameters is not necessarily the most intuitive way. That is, the sampling distribution to use is riot something obvious such as a normal or uniform distribution. The main objective of this paper is to demonstrate that these "counterintuitive" predictions of the theory are not just mathematical possibilities but actually admit physical realizations.
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