Barak Fishbain

Real-time 2D to 3D video conversion

We present a real-time implementation of 2D to 3D video conversion using compressed video. In our method, compressed 2D video is analyzed by extracting motion vectors. Using the motion vector maps, depth maps are built for each frame and the frames are segmented to provide object-wise depth ordering. These data are then used to synthesize stereo pairs. 3D video synthesized in this fashion can be viewed using any stereoscopic display. In our implementation, anaglyph projection was selected as the 3D visualization method, because it is mostly suited to standard displays.

On the feasibility of measuring urban air pollution by wireless distributed sensor networks

Accurate evaluation of air pollution on human-wellbeing requires high-resolution measurements. Standard air quality monitoring stations provide accurate pollution levels but due to their sparse distribution they cannot capture the highly resolved spatial variations within cities. Similarly, dedicated field campaigns can use tens of measurement devices and obtain highly dense spatial coverage but normally deployment has been limited to short periods of no more than few weeks. Nowadays, advances in communication and sensory technologies enable the deployment of dense grids of wireless distributed air monitoring nodes, yet their sensor ability to capture the spatiotemporal pollutant variability at the sub-neighborhood scale has never been thoroughly tested. This study reports ambient measurements of gaseous air pollutants by a network of six wireless multi-sensor miniature nodes that have been deployed in three urban sites, about 150 m apart. We demonstrate the networks capability to capture spatiotemporal concentration variations at an exceptional fine resolution but highlight the need for a frequent in-situ calibration to maintain the consistency of some sensors. Accordingly, a procedure for a field calibration is proposed and shown to improve the systems performance. Overall, our results support the compatibility of wireless distributed sensor networks for measuring urban air pollution at a sub-neighborhood spatial resolution, which suits the requirement for highly spatiotemporal resolved measurements at the breathing-height when assessing exposure to urban air pollution.

Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?

The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high spatial resolution in near-real-time and provide new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of capabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms are often of questionable quality. We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions. Our results show that their performance varies spatially and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results show that the performance varies from unit to unit, which makes it necessary to examine the data quality of each node before its use. In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to marketing these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air quality legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale, for purposes such as awareness raising). Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. In general, while low-cost platforms present low accuracy for regulatory or health purposes they can provide relative and aggregated information about the observed air quality.

Real time turbulent video perfecting by image stabilization and super-resolution

The paper presents a real-time algorithm that compensates image distortions due to atmospheric turbulence in video sequences, while keeping the real moving objects in the video unharmed. The algorithm involves (1) generation of a “reference” frame, (2) estimation, for each incoming video frame, of a local image displacement map with respect to the reference frame, (3) segmentation of the displacement map into two classes: stationary and moving objects; (4) turbulence compensation of stationary objects. Experiments with both simulated and real-life sequences have shown that the restored videos, generated in real-time using standard computer hardware, exhibit excellent stability for stationary objects while retaining real motion.

Nuclear threat detection with mobile distributed sensor networks

The ability to track illicit radioactive source in an urban environment is critical in national security applications. To this end, two modes of operation are common: positioning individual portal monitors, and deploying a network of distributed sensors. We address here the use of multiple detectors, mounted on moving vehicles, for the purpose of detecting nuclear threats. An example scenario is that of multiple taxi cabs each carrying a detector. The detectors’ positions are known in real-time as these 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 here. The problem is shown to be solvable in polynomial time and with a combinatorial network flow algorithm. The efficiency of the algorithm enable its use in real time, and in areas containing a large number of deployed detectors. A simulation study, that takes into account false-positive and false-negatives reports from individual sensors, demonstrates the effectiveness of the algorithm in using the sensor network’s detection capabilities.

3D from Compressed 2D Video

In this paper, we present an efficient method to synthesize 3D video from compressed 2D video. The 2D video is analyzed by computing frame-by-frame motion maps. For this computation, MPEG motion vectors extraction was performed. Using the extracted motion vector maps, the video undergoes analysis and the frames are segmented to provide object-wise depth ordering. The frames are then used to synthesize stereo pairs. This is performed by resampling the video frames on a grid that is governed by a corresponding depth-map. In order to improve the quality of the synthetic video, as well as to enable 2D viewing where 3D visualization is not possible, several techniques for image enhancement are used. In our test case, anaglyph projection was selected as the 3D visualization method, as the method is mostly suited to standard displays. The drawback of this method is ghosting artifacts. In our implementation we minimize these unwanted artifacts by modifying the computed depth-maps using non-linear transformations. Defocusing of one anaglyph color component was also used to counter such artifacts. Our results show that the suggested methods enable synthesis of high quality 3D videos in real-time.

Superresolution in turbulent videos: making profit from damage

It is shown that one can make use of local instabilities in turbulent video frames to enhance image resolution beyond the limit defined by the image sampling rate. We outline the processing algorithm, present its experimental verification on simulated and real-life videos, and discuss its potentials and limitations.

Visual assessment of pedestrian crashes

Of the numerous factors that play a role in fatal pedestrian collisions, the time of day, day of the week, and time of year can be significant determinants. More than 60% of all pedestrian collisions in 2007 occurred at night, despite the presumed decrease in both pedestrian and automobile exposure during the night. Although this trend is partially explained by factors such as fatigue and alcohol consumption, prior analysis of the Fatality Analysis Reporting System database suggests that pedestrian fatalities increase as light decreases after controlling for other factors. This study applies graphical cross-tabulation, a novel visual assessment approach, to explore the relationships among collision variables. The results reveal that twilight and the first hour of darkness typically observe the greatest frequency of pedestrian fatal collisions. These hours are not necessarily the most risky on a per mile travelled basis, however, because pedestrian volumes are often still high. Additional analysis is needed to quantify the extent to which pedestrian exposure (walking/crossing activity) in these time periods plays a role in pedestrian crash involvement. Weekly patterns of pedestrian fatal collisions vary by time of year due to the seasonal changes in sunset time. In December, collisions are concentrated around twilight and the first hour of darkness throughout the week while, in June, collisions are most heavily concentrated around twilight and the first hours of darkness on Friday and Saturday. Friday and Saturday nights in June may be the most dangerous times for pedestrians. Knowing when pedestrian risk is highest is critically important for formulating effective mitigation strategies and for efficiently investing safety funds. This applied visual approach is a helpful tool for researchers intending to communicate with policy-makers and to identify relationships that can then be tested with more sophisticated statistical tools.

Sensory carbon fiber based textile-reinforced concrete for smart structures

This article investigates the feasibility of intelligent textile-reinforced concrete structural elements with sensing capabilities. The concept is based on dual use of glass and carbon fiber textiles as reinforcement and, at the same time, as a sensory agent. Experimental investigation demonstrates the feasibility of the concept in two applications: detecting strains in a mechanically loaded textile-reinforced concrete beam and monitoring the interaction of the structural element with a wet environment. By detecting the changes to the integrative electrical resistance of the carbon tow, the ability of the textile to sense strain and exposure to water is demonstrated. For strain sensing, the hybrid reinforcing textile provides electro-mechanical sensing with a gauge factor of the order of 1 and a detectable correlation with the load, strain, and displacement responses. For the detection of wetting, the implementation of the carbon tow in a Wheatstone bridge detects fractional resistance changes in the order of 10−5, a figure that is effectively detected by monitoring the voltage across the bridge. The response to wetting, which is conditioned by the cracking of the beam and the exposure to ionic conductive solutions, provides a mean to monitor the functionality and the structural health of the textile-reinforced concrete beam.

The Supervised Normalized Cut Method for Detecting, Classifying, and Identifying Special Nuclear Materials

The detection of illicit nuclear materials is a major tool in preventing and deterring nuclear terrorism. The detection task is extremely difficult because of physical limitations of nuclear radiation detectors, shielding by intervening cargo materials, and the presence of background noise. We aim at enhancing the capabilities of detectors with algorithmic methods specifically tailored for nuclear data. This paper describes a novel graphtheory-based methodology for this task. This research considers for the first time the utilization of supervised normalized cut (SNC) for data mining and classification of measurements obtained from plastic scintillation detectors that are of particularly low resolution. Specifically, the situation considered here is for when both energy spectra and the time dependence of such data are acquired. We present here a computational study, comparing the supervised normalized cut method with alternative classification methods based on support vector machine (SVM), specialized feature-reducing SVMs (i.e., 1-norm SVM, recursive feature elimination SVM, and Newton linear program SVM), and linear discriminant analysis (LDA). The study evaluates the performance of the suggested method in binary and multiple classification problems of nuclear data. The results demonstrate that the new approach is on par or superior in terms of accuracy and much better in computational complexity to SVM (with or without dimension or feature reduction) and LDA with principal components analysis as preprocessing. For binary and multiple classifications, the SNC method is more accurate, more robust, and is computationally more efficient by a factor of 2‐80 than the SVM-based and LDA methods.