Ricciardi P.,National Gallery of Art |
Delaney J.K.,National Gallery of Art |
Delaney J.K.,George Washington University |
Facini M.,National Gallery of Art |
And 4 more authors.
Angewandte Chemie - International Edition | Year: 2012
In situ analysis: Near infrared imaging spectroscopy (1000-2500 nm) is used to map the use of a fat-containing paint binder, likely egg yolk, in situ on a work of art for the first time. The identification of the use of egg tempera on a 15th century illuminated manuscript leaf (Praying Prophet by Lorenzo Monaco) sheds light on the relationship between painters and illuminators and can inform preservation decisions. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Oreifej O.,University of Central Florida |
Shu G.,University of Central Florida |
Pace T.,Night Vision and Electronic Sensors Directorate |
Shah M.,University of Central Florida
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2011
Several attempts have been lately proposed to tackle the problem of recovering the original image of an underwater scene using a sequence distorted by water waves. The main drawback of the state of the art  is that it heavily depends on modelling the waves, which in fact is ill-posed since the actual behavior of the waves along with the imaging process are complicated and include several noise components; therefore, their results are not satisfactory. In this paper, we revisit the problem by proposing a data-driven two-stage approach, each stage is targeted toward a certain type of noise. The first stage leverages the temporal mean of the sequence to overcome the structured turbulence of the waves through an iterative robust registration algorithm. The result of the first stage is a high quality mean and a better structured sequence; however, the sequence still contains unstructured sparse noise. Thus, we employ a second stage at which we extract the sparse errors from the sequence through rank minimization. Our method converges faster, and drastically outperforms state of the art on all testing sequences even only after the first stage. © 2011 IEEE.
Friedman M.,Night Vision and Electronic Sensors Directorate
Optical Engineering | Year: 2013
The probability P(t) of target acquisition, for a single observer who has unlimited time to search a field of view (FOV) for a single target, is expressed in terms of search parameters P∞ and τ under conditions where these parameters are independent of time. It has been assumed that P ∞ has been determined for a particular target, scene clutter and imaging system and, for a given scenario, τ is determined empirically from P∞. The equation for P(t) is then extended to include time-limited search and field of regard (FOR) search, where it is assumed the target has an equal probability of being anywhere in the FOR. Equations are derived for the mean time to find a target for two cases: (1) an arbitrary number of observers using a single sensor search a single FOV or FOR for a single target; (2) two observers using two sensors search independently for a single target. The condition that P∞ and τ be independent of time is relaxed and this leads to the time dependent search parameter (TDSP) search model. The TDSP search model is used to calculate P(t) in: (1) search from a moving vehicle, (2) FOR search where the condition that the target has an equal probability of being anywhere in the FOR is relaxed, and (3) in multitarget search. © 2013 Society of Photo-Optical Instrumentation Engineers (SPIE).
Thotla V.,Missouri University of Science and Technology |
Ghasr M.T.A.,Missouri University of Science and Technology |
Zawodniok M.,Missouri University of Science and Technology |
Jagannathan S.,Missouri University of Science and Technology |
Agarwal S.,Night Vision and Electronic Sensors Directorate
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012
Traditional approach of locating devices relies on "tagging" with a special tracking device, for example GPS receiver. This process of tagging is often impractical and costly since additional devices may be necessary. Conversely, in many applications it is desired to track electronic devices, which already emit unintentional, passive radio frequency (RF) signals. These emissions can be used to detect and locate such electronic devices. Existing schemes often rely on a priori knowledge of the parameters of RF emission, e.g. frequency profile, and work reliably only on short distances. In contrast, the proposed methodology aims at detecting the inherent self-similarity of the emitted RF signal by using Hurst parameter, which (1) allows detection of unknown (not-pre-profiled) devices, (2) extends the detection range over signal strength (peak-detection) methods, and (3) increases probability of detection over the traditional approaches. Moreover, the distance to the device is estimated based on the Hurst parameter and passive RF signal measurements such that the detected device can be located. Theoretical and experimental studies demonstrate improved performance of the proposed methodology over existing ones, for instance the basic received signal strength (RSS) indicator scheme. The proposed approach increases the detection range by 70%, the probability of detection by 60%, and improves the range estimation and localization accuracy by 70%. © 2012 SPIE.
Brown J.B.,University of Memphis |
Chari S.,University of Memphis |
Hutchison J.,Night Vision and Electronic Sensors Directorate |
Gabonia J.,Night Vision and Electronic Sensors Directorate |
Jacobs E.,University of Memphis
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010
This paper describes the development of linear pyroelectric array systems for classification of human, animal, and vehicle targets. The pyroelectric array is simulated to produce binary profiles of targets. The profiles are classified based on height to width ratio using Naïve Bayesian classifiers. Profile widths of targets can vary due to the speed of the target. Target speeds were calculated using two techniques; two array columns, and a tilted array. The profile width was modified by the calculated speeds to show an improvement in classification results. © 2010 SPIE.