@article {221, title = {Generalized probabilistic scale space for image restoration}, journal = {IEEE Transactions on Image Processing}, volume = {19}, year = {2010}, pages = {2774 - 2780}, abstract = {

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.

}, 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} } @inproceedings {253, title = {Image synthesis using conditional random fields}, booktitle = {17th IEEE International Conference on Image Processing (ICIP)}, year = {2010}, abstract = {

Methods of scientific imaging and image analysis have become pervasive in a great variety of fields, including the properties of porous media. To study the large-scale morphological properties of porous media, high resolution random (Monte Carlo) samples are required. The purpose of this paper is to propose a novel approach for the statistical synthesis of scientific images, based on the concept of Conditional Random Fields. We explore two different sets of potential functions are used to model the pore-structure characteristics, and Monte Carlo Markov chain methods are also used to sample the high resolution images from the trained model. The resulting images are of high quality, and show the performance of the proposed framework.

}, keywords = {Conditional Random Fields, image analysis, image classification, image sampling, image synthesis, large-scale morphological properties, Monte Carlo methods, porous media, Scientific Imaging}, doi = {http://dx.doi.org/10.1109/ICIP.2010.5653134}, author = {E Ahmadi and Z Azimifar and P Fieguth and S Ayatollahi} } @inproceedings {340, title = {Statistical fusion and sampling of scientific images}, booktitle = {15th IEEE International Conference on Image Processing}, year = {2008}, abstract = {

Porous media are an important class of heterogeneous materials possessing complex random structures. Due to the limitation in measuring high resolution real samples, studying different physical properties of porous media requires the reconstruction of artificial samples. In many cases of significant interest, we have a two-scale reconstruction, in which only the large scales are resolved by low resolution measurements, leaving fine scales to be inferred from statistical models. In this paper we propose a statistical fusion approach for a two-scale porous media reconstruction, in which low resolution measurements are fused with high resolution samples, with synthetic realizations generated by posterior sampling.
}, keywords = {artificial samples, complex random structures, heterogeneous materials possessing, image fusion, image reconstruction, image resolution, image sampling, low resolution measurements, porous media, Posterior sampling, scientific image sampling, statistical analysis, statistical fusion approach, two-scale porous media reconstruction, two-scale reconstruction}, doi = {http://dx.doi.org/10.1109/ICIP.2008.4712004}, author = {A Mohebi and P Fieguth} } @inproceedings {760, title = {Hierarchical annealing for scientific models}, booktitle = {37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2004}, address = {Montreal}, abstract = {

The computational complexity of simulated annealing makes it an impractical tool in many applications, particularly for complex, non-local models on very large 2D and 3D domains as desired in many scientific contexts. In particular, it is very difficult to produce large scale structure from a fine, pixellated lattice. Thus a hierarchical approach is intuitively attractive. However, existing approaches are few and limited. Motivated by a current problem in porous media, we develop a hierarchical approach to complex model sampling. In experiments, this approach results in 1-2 orders of magnitude computational gain, and significant gains in convergence as well.

}, keywords = {2D domains, 3D domains, complex model sampling, computational complexity, data visualisation, hierarchical annealing, hierarchical sampling, image resolution, image sampling, pixellated lattice, porous materials, porous media, sampled images, scientific models, simulated annealing}, doi = {http://dx.doi.org/10.1109/ICASSP.2004.1326474}, author = {S K. Alexander and P Fieguth and E Vrscay} } @inproceedings {821, title = {Hierarchical posterior sampling for Gauss-Markov random fields}, booktitle = {2003 International Conference on Image Processing}, year = {2003}, abstract = {

The estimation of images and random fields from sparse and/or noisy data is highly-developed, to the point where methods such as least-squares estimation, simulated annealing, and wavelet shrinkage are quite standardized. The key problem, however, is that the estimates are not a realistic version of the random field, and do not represent a typical or representative sample of the system being studied. Instead, what is often desired is that we find a random sample from the posterior distribution, a much more subtle and difficult problem than estimation. Typically this is solved using Markov-Chain Monte-Carlo / simulated annealing approaches, however these may be computationally challenging and slow to converge. In this paper we use hierarchical models to formulate a novel, fast posterior sampler.

}, keywords = {image estimation, image hierarchical posterior sampling, image sampling, least squares approximations, least-squares estimation, Markov processes, Markov-chain, Monte Carlo methods, Monte-Carlo method, noisy data, posterior distribution, random field, simulated annealing, wavelet shrinkage}, doi = {http://dx.doi.org/10.1109/ICIP.2003.1247088}, author = {P Fieguth} } @inproceedings {650, title = {Parametric contour estimation by simulated annealing}, booktitle = {IEEE International Conference on Image Processing}, year = {2003}, address = {Spain}, abstract = {

Virtually all implementations of simulated annealing are simplified by assuming discrete unknowns, however continuous-parameter annealing has many potential applications to image processing. Widely scattered problems such as formant tracking, boundary estimation and phase- unwrapping can all be approached as the annealed minimizations of continuous B-spline parameters. The benefits of simulated annealing are well known, including an insensitivity to initial conditions and the ability to solve problems with many local minima. Discrete variable annealing has seen broad application, however continuous-variable annealing is limited by the computational challenge of Gibbs sampling. In this paper we develop efficient approaches to sampling, illustrated in the context of contour tracking in noisy images.

}, keywords = {boundary estimation, continuous B-spline parameters, continuous-variable annealing, discrete variable annealing, formant tracking, Gibbs sampling, image processing, image sampling, noisy images, parametric contour estimation, phase-unwrapping, simulated annealing, splines (mathematics), tracking}, doi = {http://dx.doi.org/10.1109/ICIP.2003.1247278}, author = {M Jamieson and P Fieguth and L J. Lee} } @inproceedings {591, title = {Adaptive color image segmentation using Markov random fields}, booktitle = {International Conference on Pattern Recognition}, year = {2002}, address = {Rochester, NY}, abstract = {

A new framework for color image segmentation is introduced generalizing the concepts of point-based and spatially-based methods. This framework is based on Markov random fields using a continuous Gibbs sampler. The Markov random fields approach allows for a rigorous computational framework where local and global spatial constraints can be globally optimized. Using a continuous Gibbs sampler enables the algorithm to adapt continuous-valued regional prototypes in a manner analogous to vector quantization while the discrete Gibbs sampler is used to adjust region boundaries.

}, keywords = {adaptive color image segmentation, adaptive signal processing, continuous Gibbs sampler, continuous-valued regional prototypes, discrete Gibbs sampler, global spatial constraints, image colour analysis, image sampling, image segmentation, local spatial constraints, Markov processes, Markov Random Fields, point-based methods, random processes, region boundaries adjustment, spatially-based methods, vector quantization}, doi = {http://dx.doi.org/10.1109/ICIP.2002.1039085}, author = {S Wesolkowski and P Fieguth} }