&lt;title&gt;Validation of the use of synthetic imagery for camouflage effectiveness assessment&lt;/title&gt;

Newman, Sarah; Gilmore, Marilyn A.; Moorhead, Ian R.; Filbee, David R.

doi:10.1117/12.478821

Cited by 5 publications

(4 citation statements)

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“…Multispectral camouflage becomes increasingly important with the increasing availability of sensors operating across the entire electromagnetic spectrum. Physics-based broadband scene rendering tools like CAMEO-SIM [22,23] can be valuable tools for the development and assessment of multispectral camouflage.…”

Section: Physical Measuresmentioning

confidence: 99%

“…An accurate camouflage simulation program could effectively support, and possibly replace, field trials. Prior to its use, synthetic imagery must be validated to ensure its fidelity [23,24]. When the camouflage is targeted against sensor threats operating in other than visible spectra (e.g., LWIR) a good simulation requires accurate modelling of the scene as well as the sensor.…”

Section: Real Vs Synthetic Imagerymentioning

confidence: 99%

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Review of camouflage assessment techniques

Toet

Hogervorst

2020

Target and Background Signatures VI

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In military operations signature reduction techniques such as camouflage nets, low-emissive paints, and camouflage patterns are typically deployed to optimize the survivability of high value assets by minimizing their detectability. Various methods have been developed to assess the effectiveness of these camouflage measures. There are three main approaches to the evaluation of camouflage measures: (1) a subjective approach through observer experiments, (2) an objective computational approach through image analysis, and (3) an objective approach through physical measurements. Although subjective evaluation methods have a direct relation with the operational practice, they are often difficult to implement because of time and budget restrictions (these experiments typically require a lot of observers, repetitions and different targets and backgrounds), or simply because the associated conditions are not safe for the observers. The objective evaluation methods are typically based on the outcome of psychophysical laboratory experiments using simple artificial stimuli, presented under extremely restricted (impoverished) conditions, and in different experimental paradigms. Other objective methods are based on signal processing techniques and have no obvious counterpart in human vision. Only a few attempts have been made to validate any of these objective metrics against the performance of human observers in realistic military scenarios. As a result, there are currently no standard and internationally accepted methods and procedures to evaluate camouflage equipment and techniques, and to indicate their military effectiveness. In this review paper we present an overview of the various subjective (psychophysical) and objective (computational, image or video based) evaluation methods that are currently available and that have been used to validate camouflage effectiveness. In addition, we discuss the relative merits of field experiments versus laboratory experiments.

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Section: Physical Measuresmentioning

confidence: 99%

Section: Real Vs Synthetic Imagerymentioning

confidence: 99%

Review of camouflage assessment techniques

Toet

Hogervorst

2020

Target and Background Signatures VI

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“…Furthermore, textural features, except the lowest order statistical moments, were not analyzed in these validation works. Other efforts to validate CAMEO-SIM with respect to fundamental thermal and visual properties have been made by DSTL 5 .…”

Section: Introductionmentioning

confidence: 99%

Methods for validation of optical signature models

Nelsson

Andersson

Boerjesson

et al. 2005

Targets and Backgrounds XI: Characterization and Representation

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When using prediction programs for optical signatures, it is necessary to include validations to find estimates of the uncertainties and define the regions of validity. In this paper we present two paths of development of validation methods: The objective of the first path is to analyze and validate the differences between simulated and measured images, through image features such as edge concentration and different energy measures. In particular, aspects that are important for detection, classification and identification of targets are considered. The second path concerns development of methods for quantifying the propagation of input data uncertainties to output parameters in computational predictions. Two commercial codes have been used for the modeling: RadThermIR for thermal predictions of the targets and CAMEO-SIM for the radiometry and rendering. A recently developed interface between the two codes has been utilized. For the validation of spatial statistics, several feature values have been computed for a measured image and for the corresponding simulated image. It was found that the agreement was quite good. The work on propagation of uncertainties in computational predictions has resulted in a number of proposed methods. In this paper we present two different methods: one based on linear error propagation and one based on the Monte Carlo method. The results are according to expectations for both types of methods and show that a large part of the uncertainty in predicted temperature emanates from input parameter uncertainties for the considered test case.

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“…Studies on prediction of the infrared (IR) signature of objects [1][2][3] and the IR scene from objects over a background [4][5][6] have been performed continuously. And these results need to compare with experimental results in order to evaluate their adequacy.…”

Section: Introductionmentioning

confidence: 99%

Study on inverse estimation of radiative reflection properties in mid-wavelength infrared region by using the repulsive particle swarm optimization algorithm

2013

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In this study, we develop software that estimates radiative properties of painted surfaces in the mid-wavelength infrared (MWIR) region inversely from the measured temperature and radiance variations with time by applying the repulsive particle swarm optimization algorithm. In this study the radiance in the MWIR region and surface temperature are obtained from a commercial software considering winter weather, and these results are used to estimate radiative reflection properties. Surface radiative reflection properties for three different paints are estimated by using the predetermined radiance in the MWIR region and surface temperature. This finding suggests that the process for obtaining surface radiative properties proposed in this study could be a useful way to obtain infrared signatures from objects with various surface coatings of unknown radiative properties.

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<title>Validation of the use of synthetic imagery for camouflage effectiveness assessment</title>

Cited by 5 publications

References 0 publications

Review of camouflage assessment techniques

Review of camouflage assessment techniques

Methods for validation of optical signature models

Study on inverse estimation of radiative reflection properties in mid-wavelength infrared region by using the repulsive particle swarm optimization algorithm

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