Mr. Michael C. Simon Profile

Michael C. Simon

at 21st Century Systems Inc

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Publications (3)

Proceedings Article | 26 May 2011 Paper

Video enhancement effectiveness for target detection

Michael Simon, Amber Fischer, Plamen Petrov

Proceedings Volume 8020, 80200W (2011) https://doi.org/10.1117/12.884137

KEYWORDS: Video, Video surveillance, Detection and tracking algorithms, Unmanned aerial vehicles, Denoising, Air contamination, Target detection, Cameras, Filtering (signal processing), Surveillance

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Proceedings Article | 14 May 2007 Paper

Towards distributed ATR using subjective logic combination rules with a swarm of UAVs

Stephen O'Hara, Michael Simon, Qiuming Zhu

Proceedings Volume 6561, 65611G (2007) https://doi.org/10.1117/12.718321

KEYWORDS: Unmanned aerial vehicles, Automatic target recognition, Logic, Video, Cameras, Video processing, Video surveillance, Sensors, Surveillance, Target recognition

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In this paper, we present our initial findings demonstrating a cost-effective approach to Aided Target Recognition (ATR) employing a swarm of inexpensive Unmanned Aerial Vehicles (UAVs). We call our approach Distributed ATR (DATR). Our paper describes the utility of DATR for autonomous UAV operations, provides an overview of our methods, and the results of our initial simulation-based implementation and feasibility study. Our technology is aimed towards small and micro UAVs where platform restrictions allow only a modest quality camera and limited on-board computational capabilities. It is understood that an inexpensive sensor coupled with limited processing capability would be challenged in deriving a high probability of detection (P_d) while maintaining a low probability of false alarms (P_fa). Our hypothesis is that an evidential reasoning approach to fusing the observations of multiple UAVs observing approximately the same scene can raise the P_d and lower the P_fa sufficiently in order to provide a cost-effective ATR capability. This capability can lead to practical implementations of autonomous, coordinated, multi-UAV operations. In our system, the live video feed from a UAV is processed by a lightweight real-time ATR algorithm. This algorithm provides a set of possible classifications for each detected object over a possibility space defined by a set of exemplars. The classifications for each frame within a short observation interval (a few seconds) are used to generate a belief statement. Our system considers how many frames in the observation interval support each potential classification. A definable function transforms the observational data into a belief value. The belief value, or opinion, represents the UAV's belief that an object of the particular class exists in the area covered during the observation interval. The opinion is submitted as evidence in an evidential reasoning system. Opinions from observations over the same spatial area will have similar index values in the evidence cache. The evidential reasoning system combines observations of similar spatial indexes, discounting older observations based upon a parameterized information aging function. We employ Subjective Logic operations in the discounting and combination of opinions. The result is the consensus opinion from all observations that an object of a given class exists in a given region.

Proceedings Article | 9 April 2007 Paper

Collective agents interpolative integral (CAII) for asymmetric threat detection

Qiuming Zhu, Stephen O'Hara, Michael Simon, Eric Lindahl, Plamen Petrov

Proceedings Volume 6571, 65710I (2007) https://doi.org/10.1117/12.718322

KEYWORDS: Data modeling, Data fusion, Probability theory, Information fusion, Systems modeling, Computing systems, Computer architecture, Logic, Mathematical modeling, Prototyping

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