@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} } @article {282, title = {A systematic approach to feature tracking of lumbar spine vertebrae from fluoroscopic images using complex-valued wavelets}, journal = {Computer Methods in Biomechanics and Biomedical Engineering}, volume = {12}, year = {2009}, abstract = {

This paper presents a systematic approach to lumbar spine vertebrae tracking in fluoroscopic images using complex-valued wavelets. The proposed algorithm is designed specifically based on a set of performance criteria associated with the detection and tracking of feature points in lumbar spine vertebrae from fluoroscopic images. The algorithm handles contrast and illumination non-homogeneities and noise in fluoroscopic images through the use of local phase information obtained using complex-valued wavelets. The algorithm is capable of tracking feature points that undergo various geometric deformations caused during the fluoroscopic imaging process by defining a descriptor that is invariant to scale and rotation and robust to affine, projective and mild pin-cushion distortions. The algorithm has been tested using dynamic sagittal fluoroscopic videos of the lumbar-sacral region and testing results indicate that the algorithm achieves good tracking performance of lumbar spine vertebrae in fluoroscopic images that exhibit contrast and illumination non-homogeneities as well as noise, with mean root mean square error of less than 0.40 mm under in all test sequences.

}, keywords = {complex-valued wavelets, fluoroscopic images, lumbar spine, tracking, vertebrae}, doi = {http://dx.doi.org/10.1080/10255840902802891}, url = {http://www.tandfonline.com/doi/abs/10.1080/10255840902802891}, author = {A Wong and N M. Dunk and J Callaghan} } @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} }