Cristina E. Manfredotti

Relational Dynamic Bayesian Networks to Improve Multi-target Tracking

Tracking relations between moving objects is a big challenge for Computer Vision research. Relations can be useful to better understand the behaviors of the targets, and the prediction of trajectories can become more accurate. Moreover, they can be useful in a variety of situations like monitoring terrorist activities, anomaly detection, sport coaching, etc.

Foreground-to-Ghost discrimination in single-difference pre-processing

It is well known that motion detection using single frame differencing, while computationally much simpler than other techniques, is more liable to generate large areas of false foregrounds known as ghosts. In order to overcome this problem the authors propose a method based on signed differencing and connectivity analysis. The proposal is suitable to applications which cannot afford the un-avoidable errors of background modeling or the limitations of 3-frames preprocessing.

Combining Gene Expression Profiles and Drug Activity Patterns Analysis: A Relational Clustering Approach

The combined analysis of tissue micro array and drug response datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activity patterns in tumor cells. However, the amount and the complexity of biological data needs appropriate data mining models in order to extract interesting patterns and useful information. The ultimate goal of this paper is to define a model which, given the gene expression profile related to a specific tumor tissue, could help in selecting a set of most responsive drugs. This is accomplished through an integrated framework based on a constraint-based clustering algorithm, called Relational K-Means, which groups cell lines using drug response information and taking into account cell-to-cell relationships derived from their gene expression profiles.

Foreground-to-ghost discrimination in seingle-difference pre-processing

It is well known that motion detection using single frame differencing, while computationally much simpler than other techniques, is more liable to generate large areas of false foregrounds known as ghosts. In order to overcome this problem the authors propose a method based on signed differencing and connectivity analysis. The proposal is suitable to applications which cannot afford the un-avoidable errors of background modeling or the limitations of 3-frames preprocessing.In many application scenarios, the use of Regions of Interest (ROIs) within video sequences is a useful concept. It is shown in this paper how Flexible Macroblock Ordering (FMO), defined in H.264/AVC as an error resilience tool, can be used for the coding arbitrary-shaped ROIs. In order to exploit the coding of ROIs in an H.264/AVC bitstream, a description-driven content adaptation framework is introduced that is able to extract the ROIs of a given bitstream. The results of a series of tests indicate that the ROI extraction process significantly reduces the bit rate of the bitstreams and increases the decoding speed. In case of a fixed camera and a static background, the impact of this reduction on the visual quality of the video sequence is negligible. Regarding the adaptation framework itself, it is shown that in all cases, the framework operates in real time and that it is suited for streaming scenarios by design.