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CS Colloq: Barak Fishbain

Talk Title: Network flow algorithms for sensor networks and visual data analysisSpeaker: Barak FishbainHost: Prof. Cyrus ShahabiAbstract:As digital environments become increasingly complex, and the tools for managing information become increasingly advanced, it is essential to assist users in selecting their short term and long term attentional focus. In this talk a novel graph-cut based approaches for multi-dimensional data analysis are presented. These methods are highly robust and most efficient which allows for the analysis of significantly large data sets. Air quality control and video segmentation are presented as representative applications.
Air quality control is addressed by the use of sensors network, where each sensor is mounted on a moving vehicle, for the purpose of detecting various threats. An example scenario is that of multiple taxi cabs each carrying a detector. The detectors' positions are continuously reported from GPS data. The level of detected risk is then reported from each detector at each position. The problem is to delineate the presence of a potentially dangerous source and its approximate location by identifying a small area that has an elevated concentration of reported risk. This problem of using spatially deployed mobile
detector networks to identify and locate risks is modeled and formulated. Then it is shown to be solvable in polynomial time and with a combinatorial network flow algorithm. The efficiency of the algorithm enables its use in real time, and in areas containing a large number of deployed detectors.
In video segmentation a typical goal is to group together similar objects, or pixels in the case of image processing. At the same time another goal is to have each group distinctly dissimilar from the rest and possibly to have the group size fairly large. These goals are often combined as a ratio optimization problem. State-of-the-art methods address these ratio problems by employing nonlinear continuous approaches, such as spectral techniques.
These spectral techniques deliver solutions in real numbers which are not feasible to the discrete partitioning problem. Furthermore, these continuous approaches are relatively computationally expensive. In this talk a novel graph-cut based approaches for optimally solving a set of segmentation ratio problems are presented. These algorithms guarantee optimal solution to the respective problem and consistent output between different runs.
These methods are most efficient which allows for the segmentation of significantly large video data sets.
The work was done with Prof. Dorit S. Hochbaum, University of California at Berkeley.Bio:Barak Fishbain received his Ph.D in EE from Tel-Aviv University, Israel in 2008. His research interests are Computer Vision, Image Processing, Video Surveillance and Medical Imaging. Currently he is a postdoctoral fellow in the Dept. of Industrial Engineering and Operations Research in the University of California at Berkeley, USA

Location: Charles Lee Powell Hall (PHE) - 333

Audiences: Everyone Is Invited

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