@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} } @inproceedings {422, title = {A probabilistic living cell segmentation model}, booktitle = {International Conference on Image Analysis and Recognition}, year = {2005}, abstract = {

A better understanding of cell behavior is very important in drug and disease research. Cell size, shape, and motility may play a key role in stem-cell specialization or cancer development. However the traditional method of inferring these values from image sequences manually is such an onerous task that automated methods of cell tracking and segmentation are in high demanded, especially given the increasing amount of cell data being collected. In this paper, a novel probabilistic cell model is designed to segment the individual hematopoietic stem cells (HSCs) extracted from mice bone marrow cells. The proposed cell model has been successfully applied to HSC segmentation, identifying the most probable cell locations in the image on the basis of cell brightness and morphology.

}, keywords = {bone marrow cells, cancer development, cellular biophysics, disease research, hematopoietic stem cells, image segmentation, image sequences, medical image processing, probabilistic living cell segmentation model, probability, stem-cell specialization}, doi = {http://dx.doi.org/10.1109/ICIP.2005.1529956}, author = {N Nezamoddin Kachouie and L J Lee and P Fieguth} } @article {418, title = {Statistical processing of large image sequences}, journal = {IEEE Transactions on Image Processing}, volume = {14}, year = {2005}, pages = {80 - 93}, abstract = {

The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. We present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 times; 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Biological, Cluster Analysis, computational complexity, Computer Graphics, Computer Simulation, Computer-Assisted, correlation methods, correlation structure, dynamic estimation method, image enhancement, image interpretation, image sequences, Imaging, Information Storage and Retrieval, Kalman filter, Kalman filters, large-scale stochastic image sequence estimation, Models, Movement, Numerical Analysis, ocean surface temperature, Pattern Recognition, Remote Sensing, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, single-frame estimation, static estimation step, Statistical, statistical analysis, statistical image processing, stochastic processes, Subtraction Technique, Three-Dimensional, Video Recording}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2004.838703}, author = {F M. Khellah and P Fieguth and M J. Murray and M R. Allen} } @inproceedings {663, title = {Adaptive Wiener filtering of noisy images and image sequences}, booktitle = {IEEE International Conference on Image Processing}, year = {2003}, address = {Spain}, abstract = {

In this work, we consider the adaptive Wiener filtering of noisy images and image sequences. We begin by using an adaptive weighted averaging (AWA) approach to estimate the second-order statistics required by the Wiener filter. Experimentally, the resulting Wiener filter is improved by about 1 dB in the sense of peak-to-peak SNR (PSNR). Also, the subjective improvement is significant in that the annoying boundary noise, common with the traditional Wiener filter, has been greatly suppressed. The second, and more substantial, part of this paper extends the AWA concept to the wavelet domain. The proposed AWA wavelet Wiener filter is superior to the traditional wavelet Wiener filter by about 0.5 dB (PSNR). Furthermore, an interesting method to effectively combine the denoising results from both wavelet and spatial domains is shown and discussed. Our experimental results outperform or are comparable to state-of-art methods.

}, keywords = {adaptive filters, adaptive weighted averaging, adaptive Wiener filtering, Image Denoising, image sequences, noisy images, peak-to-peak SNR, second-order statistics, statistical analysis, wavelet domain, wavelet transforms, Wiener filters}, doi = {http://dx.doi.org/10.1109/ICIP.2003.1247253}, author = {F Jin and P Fieguth and L Winger and E Jernigan} } @inproceedings {595, title = {Efficient interpolation of large image sequences}, booktitle = {IEEE International Geoscience And Remote Sensing Symposium}, year = {2002}, address = {Toronto}, abstract = {

Dynamic estimation of large-scale remote-sensing image sequences is important in a variety of scientific applications. However, the growing size of such sensed images makes conventional dynamic estimation methods, for example the Kalman and related filters, impractical. In this paper we present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a large (512 times;512) image sequence of ocean surface temperature.

}, keywords = {dynamic estimation, geophysical signal processing, image sequences, interpolation, Kalman filter, Kalman filters, large image sequences, large-scale remote-sensing image sequences, ocean surface temperature, oceanographic techniques, Remote Sensing}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1027173}, author = {F M. Khellah and P Fieguth} } @inproceedings {535, title = {Motion estimation of sparse, remotely-sensed fields}, booktitle = {14th Canadian Conference on Electrical and Computer Engineering}, year = {2001}, address = {Toronto}, abstract = {

Sea surface temperature (SST) can be estimated from remotely-sensed images. Because of the sparsity of the available observation it is ideal to do estimation using dynamic methods (such as Kalman filtering). To model dynamics of SST accurately we need to know the motion of sea current. The traditional video motion estimation problem is straightforward, in some ways, because there are so few constraints. That is, the motion vectors are pretty much arbitrary, and successive image frames are densely pixellated, have the same number of pixels, with similar noise statistics. However there are many motion estimation problems, particularly in the area of remote sensing, which do not share these properties. In this paper we investigate the problem of determining the motion field of the sea surface, based on infrared measurements of surface temperature. This problem is challenging in that only a subset of the whole domain is measured at each point in time; specifically, only a few stripes are imaged. In addition, because of clouds, the measured subset varies from time to time. The quality (level of noise) can also vary from pixel to pixel. Our research is based on the following assumptions and observations: the motion field should be smooth and ideally divergence-free, i.e. the motion field is close to time-stationary. Based on these assumptions we choose to use optical flow method for this motion problem. We handle difficulty of data sparcity by pre-estimation to get a dense field. Pre-estimation can be refined by integrating this motion estimation result

}, keywords = {data sparcity, dense field, dynamic methods, geophysical signal processing, image sequences, infrared imaging, infrared measurements, Kalman filtering, motion estimation, motion field, oceanographic techniques, optical flow method, Remote Sensing, remotely-sensed images, sea current, sea surface temperature, sparse remotely-sensed fields, SST}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933601}, author = {F Jin and F M Khellah and P Fieguth and L Winger} } @inproceedings {502, title = {Incremental shape reconstruction using stereo image sequences}, booktitle = {International Conference on Image Processing}, year = {2000}, abstract = {

The limitations of estimating structure from either stereo or motion alone can be addressed by the use of stereo image sequences; however, many existing techniques for processing stereo sequences rely on having accurate feature correspondences initially. We present an iterative feature matching and shape reconstruction algorithm. The proposed method simultaneously resolves matching ambiguities by multiple hypothesis testing and develops an incrementally accurate and dense representation of the reconstructed object. This approach uses minimal domain specific constraints and can be easily generalised. The method\&$\#$39;s potential is demonstrated in a space vision application.

}, keywords = {artificial satellites, Computer Vision, dense representation, dynamic estimation, feature correspondences, Feature Extraction, image matching, image reconstruction, image representation, image sequences, incremental shape reconstruction, iterative feature matching algorithm, minimal domain specific constraints, multiple hypothesis testing, parameter estimation, reconstructed object, satellite docking, satellite rendezvous, space vision application, stereo image processing, stereo image sequences, stereo sequences processing, structure estimation}, doi = {http://dx.doi.org/10.1109/ICIP.2000.899818}, author = {T J. Moyung and P Fieguth} }