Engineering topics
Birch, Gabriel Carisle
Publications and source records attributed to Birch, Gabriel Carisle.
3D Imaging with Structured Illumination for Advanced Security Applications
Three-dimensional (3D) information in a physical security system is a highly useful discriminator. The two-dimensional data from an imaging systems fails to provide target distance and three-dimensional motion vector, which can be used to reduce nuisance alarm rates and increase system effectiveness. However, 3D imaging devices designed primarily for use in physical security systems are uncommon. This report discusses an architecture favorable to physical security systems; an inexpensive snapshot 3D imaging system utilizing a simple illumination system. The method of acquiring 3D data, tests to understand illumination design, and software modifications possible to maximize information gathering capability are discussed.
Sinusoidal Siemens star spatial frequency response measurement errors due to misidentified target centers
Numerous methods are available to measure the spatial frequency response (SFR) of an optical system. A recent change to the ISO 12233 photography resolution standard includes a sinusoidal Siemens star test target. We take the sinusoidal Siemens star proposed by the ISO 12233 standard, measure system SFR, and perform an analysis of errors induced by incorrectly identifying the center of a test target. We show a closed-form solution for the radial profile intensity measurement given an incorrectly determined center and describe how this error reduces the measured SFR of the system. As a result, using the closed-form solution, we propose a two-step process by which test target centers are corrected and the measured SFR is restored to the nominal, correctly centered values.
UAS Detection Classification and Neutralization: Market Survey 2015
The purpose of this document is to briefly frame the challenges of detecting low, slow, and small (LSS) unmanned aerial systems (UAS). The conclusion drawn from internal discussions and external reports is the following; detection of LSS UAS is a challenging problem that cannot be achieved with a single detection modality for all potential targets. Classification of LSS UAS, especially classification in the presence of background clutter (e.g., urban environment) or other non-threating targets (e.g., birds), is under-explored. Though information of available technologies is sparse, many of the existing options for UAS detection appear to be in their infancy (when compared to more established ground-based air defense systems for larger and/or faster threats). Companies currently providing or developing technologies to combat the UAS safety and security problem are certainly worth investigating, however, no company has provided the statistical evidence necessary to support robust detection, identification, and/or neutralization of LSS UAS targets. The results of a market survey are included that highlights potential commercial entities that could contribute some technology that assists in the detection, classification, and neutralization of a LSS UAS. This survey found no clear and obvious commercial solution, though recommendations are given for further investigation of several potential systems.
History and Evolution of the Johnson Criteria
The Johnson Criteria metric calculates probability of detection of an object imaged by an optical system, and was created in 1958 by John Johnson. As understanding of target detection has improved, detection models have evolved to better model additional factors such as weather, scene content, and object placement. The initial Johnson Criteria, while sufficient for technology and understanding at the time, does not accurately reflect current research into target acquisition and technology. Even though current research shows a dependence on human factors, there appears to be a lack of testing and modeling of human variability.
Characterizing Imaging Systems in Obscured Environments.
Abstract not provided.
Security camera resolution measurements: Horizontal TV lines versus modulation transfer function measurements.
The horizontal television lines (HTVL) metric has been the primary quantity used by division 6000 related to camera resolution for high consequence security systems. This document shows HTVL measurements are fundamen- tally insufficient as a metric to determine camera resolution, and propose a quantitative, standards based methodology by measuring the camera system modulation transfer function (MTF), the most common and accepted metric of res- olution in the optical science community. Because HTVL calculations are easily misinterpreted or poorly defined, we present several scenarios in which HTVL is frequently reported, and discuss their problems. The MTF metric is discussed, and scenarios are presented with calculations showing the application of such a metric.