@article {221,
	title = {Generalized probabilistic scale space for image restoration},
	journal = {IEEE Transactions on Image Processing},
	volume = {19},
	year = {2010},
	pages = {2774 - 2780},
	abstract = {<p>A novel generalized sampling-based probabilistic scale space theory is proposed for image restoration. We explore extending the definition of scale space to better account for both noise and observation models, which is important for producing accurately restored images. A new class of scale-space realizations based on sampling and probability theory is introduced to realize this extended definition in the context of image restoration. Experimental results using 2-D images show that generalized sampling-based probabilistic scale-space theory can be used to produce more accurate restored images when compared with state-of-the-art scale-space formulations, particularly under situations characterized by low signal-to-noise ratios and image degradation.</p>
},
	keywords = {2D image, Bayes Theorem, Computer-Assisted, generalized sampling-based probabilistic scale space theory, Humans, image degradation, image processing, image restoration, image sampling, Male, noise model, nonlinear dynamics, Normal Distribution, Photography, probability, Prostate, signal-to-noise ratio, Ultrasonography},
	issn = {1057-7149},
	doi = {http://dx.doi.org/10.1109/TIP.2010.2048973},
	author = {A Wong and A Mishra}
}
@article {690,
	title = {Gaussian MRF rotation-invariant features for image classification},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {26},
	year = {2004},
	pages = {951 - 955},
	abstract = {<p>Features based on Markov random field (MRF) models are sensitive to texture rotation. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate method, an approximate least squares estimate method is designed and implemented. Rotation-invariant features are obtained from the ACGMRF model parameters using the discrete Fourier transform. The ACGMRF model is demonstrated to be a statistical improvement over three published methods. The three methods include a Laplacian pyramid, an isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features.</p>
},
	keywords = {Algorithms, anisotropic circular Gaussian MRF model, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, discrete Fourier transform, discrete Fourier transforms, Gaussian MRF rotation-invariant features, gray level cooccurrence probability features, image classification, image enhancement, image interpretation, image texture, Laplacian pyramid, Markov Chains, Markov processes, Markov Random Field models, Models, Normal Distribution, Pattern Recognition, Rotation, rotation-invariant texture features, Statistical, statistical improvement, texture rotation},
	issn = {0162-8828},
	doi = {http://dx.doi.org/10.1109/TPAMI.2004.30},
	author = {H Deng and D A. Clausi}
}
