@inproceedings {254, title = {Model-based tracking: Temporal conditional random fields}, booktitle = {17th IEEE International Conference on Image Processing (ICIP)}, year = {2010}, abstract = {

We present Temporal Conditional Random Fields, a probabilistic framework for modeling object motion. The state-of-the-art discriminative approach for tracking is known as dynamic conditional random fields. This method models an event based on spatial and temporal relation between pixels in an image sequence without any prediction. To facilitate such a powerful graphical model with prediction and come up with a CRF-based predictor, we propose a set of new temporal relations for object tracking, with feature functions such as optical flow (calculated among consequent frames) and line filed features. We validate our proposed method with real data sequences and will show that the TCRF prediction is nearly equivalent with result of template matching. Experimental results indicate that our TCRF can predict future state of any maneuvering target with nearly zero error during its constant motion. Not only the proposed TCRF has a simple and easy to implement structure, but also it outperforms the state-of-the-art predictors such as Kalman filter.

}, keywords = {constant motion, CRF-based predictor, image sequence, image sequences, model-based tracking, motion compensation, object motion modeling, probabilistic framework, probability, temporal conditional random fields, tracking}, doi = {http://dx.doi.org/10.1109/ICIP.2010.5653823}, author = {M J. Shafiee and Z Azimifar and P Fieguth} } @inproceedings {332, title = {Background estimation for microscopic cellular images}, booktitle = {15th IEEE International Conference on Image Processing, 2008}, year = {2008}, abstract = {

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.

}, 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} } @inproceedings {361, title = {Automated detection of mitosis in embryonic tissues}, booktitle = {4th Annual Canadian Conference on Computer and Robot Vision}, year = {2007}, month = {05/2007}, address = {Montreal, Quebec, Canada}, abstract = {

Characterization of mitosis is important for understanding the mechanisms of development in early stage embryos. In studies of cancer, another situation in which mitosis is of interest, the tissue is stained with contrast agents before mitosis characterization; an intervention that could lead to atypical development in live embryos. A new image processing algorithm that does not rely on the use of contrast agents was developed to detect mitosis in embryonic tissue. Unlike previous approaches that uses still images, the algorithm presented here uses temporal information from time-lapse images to track the deformation of the embryonic tissue and then uses changes in intensity at tracked regions to identify the locations of mitosis. On a one hundred minute image sequence, consisting of twenty images, the algorithm successfully detected eighty-one out of the ninety-five mitosis. The performance of the algorithm is calculated using the geometric mean measure as 82\%. Since no other method to count mitoses in live tissues is known, comparisons with the present results could not be made.

}, keywords = {automated mitosis detection, cancer, early stage embryo, embryonic tissues, image processing algorithm, image sequence, image sequences, medical image processing, time-lapse image}, doi = {http://dx.doi.org/10.1109/CRV.2007.11}, author = {P Siva and G W. Brodland and D A. Clausi} }