@inproceedings {21, title = {HIGH DYNAMIC RANGE MAP ESTIMATION VIA FULLY CONNECTED RANDOM FIELDS WITH STOCHASTIC CLIQUES}, booktitle = {International Conference on Image Processing}, year = {2015}, abstract = {

The reconstruction of high dynamic range (HDR) images via conventional camera systems and low dynamic range (LDR) images is a growing field of research in image acquisition. The radiance map associated with the HDR image of a scene is typically computed using multiple images of the same scene captured at different exposures (i.e., bracketed LDR images). This approach, though inexpensive, is sensitive to noise under high camera ISO. Each bracketed image is associated with a different level of noise due to the change in exposure time, and the noise is further amplified when tone-mapping the HDR image for display. A new framework is proposed to address the associated noise in the context of random fields. The estimation of the HDR image from a set of LDR images is formulated as a stochastically fully connected conditional random field where the spatial information is incorporated to compute the HDR value in combination with the LDR image values. Experimental results show that the proposed framework compensated the non-stationary ISO noise while preserving

}, keywords = {Conditional Random Fields, HDR Reconstruction, High Dynamic Range Imaging, Image Denoising, SFCRF}, author = {F Li and M J. Shafiee and A Chung and B Chwyl and F Kazemzadeh and A Wong and J S. Zelek} } @article {101, title = {How Conditional Random Fields Learn Dynamics: An Example-Based Study}, journal = {Computer Communication \& Collaboration}, volume = {1}, year = {2013}, abstract = {

In this paper we investigate how Conditional Random Fields (CRFs) learn dynamics. To demonstrate the ability of CRF in learning dynamics, a discriminative probabilistic framework, Temporal Conditional Random Fields, is presented for the modeling of the object motion and tracking. The main drawback of generative models, such as HMM and MRF is that they can simply employ the relation between states without considering the relation between states and measurements, while discriminative frameworks can model any arbitrary relation between measurements and states. To facilitate such a powerful graphical model to learn the object motion, and to achieve a CRF-based estimation based upon the advantage of the discriminative framework, a set of graphical temporal relations is proposed for the object tracking, including feature functions, such as optical flow (calculated based upon consequent frames) and line field features. Based on temporal feature function, we show the ability of CRF in dynamic learning mathematically. As it is assumed that the object motion is nearly constant and that the current measurement is not available when TCRF estimates the target state, therefore, the changing of the object motion is addressed by utilizing a template matching in order to determine and then retrain TCRF. The proposed method is validated using synthetic and real data sequences. This shows that the TCRF estimation error is approximately zero.

}, keywords = {Conditional Random Fields, Discriminative Models, Motion Dynamic, Potential Functions, Visual Tracking}, author = {M J. Shafiee and Z Azimfar and P Fieguth} } @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} }