@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} }