@inproceedings {253,
	title = {Image synthesis using conditional random fields},
	booktitle = {17th IEEE International Conference on Image Processing (ICIP)},
	year = {2010},
	abstract = {<p>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.</p>
},
	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 {332,
	title = {Background estimation for microscopic cellular images},
	booktitle = {15th IEEE International Conference on Image Processing, 2008},
	year = {2008},
	abstract = {<p>Background modelling is a key task in tracking applications. Our interest in this paper is the accurate estimation of static backgrounds in scientific imaging, such as those in automated stem cell tracking. In this paper, an effective background estimation method is proposed. First, the segmentation results are used to remove the foreground objects, then the background is robustly estimated over the resultant 3-Dimensional residual image sequence. We do spatio-temporal background estimation over a local neighbourhood with a robust trimmed mean. The experimental results generated by the proposed method are quite promising.</p>
},
	keywords = {automated stem cell tracking, Biomedical Imaging, cellular biophysics, foreground objects removal, image segmentation, image sequence, image sequences, medical image processing, microscopic cellular images, Scientific Imaging, spatio-temporal background estimation, static backgrounds, tracking applications},
	doi = {http://dx.doi.org/10.1109/ICIP.2008.4712436},
	author = {N Nezamoddin Kachouie and P Fieguth}
}
