&lt;title&gt;Objective assessment of simulated daytime and NVG image fidelity&lt;/title&gt;

Toet, Alexander; Kooi, Frank L.; Kuijper, F.; Smeenk, Roland J. M.

doi:10.1117/12.602463

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…Visual conspicuity has for instance successfully been deployed to measure saliency of camouflaged targets both in the lab ( Figure 6) [9,47,48] and in the field [48,49], for the validation of simulated visual, nearinfrared and thermal imagery [88,89] (see Figure 7), and to assess the detectability of targets in thermal imagery [49] (see Figure 8).…”

Section: Visual Conspicuitymentioning

confidence: 99%

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: Visual Conspicuitymentioning

confidence: 99%

Review of camouflage assessment techniques

Toet

Hogervorst

2020

Target and Background Signatures VI

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“…The resulting conspicuity measures reliably predict human visual search performance in realistic and complex scenarios 9,11 for both static targets 11 and for dynamic targets 13 , in the lab 11,14,15 and in the field 11,16 . Visual conspicuity can also quantify the saliency of camouflaged targets in visual, near-infrared and thermal imagery [16][17][18] . Depending on the criterion used to assess a target's conspicuity between detection and identification conspicuity.…”

Section: Observer Camouflage Metricsmentioning

confidence: 99%

Measuring the dynamics of camouflage in natural scenes using convolutional neural networks

Burg

Hogervorst²,

Toet³

2022

Target and Background Signatures VIII

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Natural scenes are typically highly heterogeneous, making it difficult to assess camouflage effectiveness for moving objects since their local contrast varies with their momentary position. Camouflage performance is usually assessed through visual search and detection experiments involving human observers. However, such studies are time-consuming and expensive since they involve many observers and repetitions. Here, we show that a (state-of-the-art) convolutional neural network (YOLO) can be applied to measure the camouflage effectiveness of stationary and moving persons in a natural scene. The network is trained on human observer data. For each detection, it also provides the probability that the detected object is correctly classified as a person, which is directly related to camouflage effectiveness: more effective camouflage yields lower classification probabilities. By plotting the classification probability as a function of a person's position in the scene, a 'camouflage efficiency heatmap' is obtained, that reflects the variation of camouflage effectiveness over the scene. Such a heatmap can for instance be used to determine locations in a scene where the person is most effectively camouflaged. Also, YOLO can be applied dynamically during a scene traversal, providing real-time feedback on a person's detectability. We compared the YOLO-predicted classification probability for a soldier in camouflage clothing moving through a rural scene to human performance. Camouflage effectiveness predicted by YOLO agrees closely with human observer assessment. Thus, YOLO appears an efficient tool for the assessment of camouflage of static as well as dynamic objects.

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“…This can be demonstrated by a person standing in the shade in a hall who might be clearly visible in the daytime by naked eye but will be undetectable when observed using an NVG at night 7 . Toet & Kooi 7 show the importance of realistic shadow rendering in NVG simulation. When shadow effects are not or incorrectly applied large differences occur in the observability of (shaded) targets.…”

Section: Introductionmentioning

confidence: 99%

NVG-the-Day: towards realistic night-vision training

Hogervorst

Kooi

2014

SPIE Proceedings

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Current night-time training using (flight-, driving-) simulators is hindered by the lack of realism. Effective night-time training requires the simulation of the illusions and limitations experienced while wearing Night Vision Goggles during the night. Various methods exist that capture certain sensor effects such as noise and the characteristic halos around lights. However, other effects are often discarded, such as the fact that image intensifiers are especially sensitive to nearinfrared (NIR) light, which makes vegetation appear bright in the image (the chlorophyll effect) and strongly affects the contrast of objects against their background. Combined with the contrast and resolution reduction in NVG imagery, a scene at night may appear totally different than during the day. In practice these effects give rise to misinterpretations and illusions. When training persons on how to deal with such illusions it is essential to simulate them as accurately as possible . We present a method based on our Colour-Fusion technique (see Toet & Hogervorst, Opt. Eng. 2012) 1 to create a realistic NVG simulation from daytime imagery, which allows for training of the typical effects experienced while wearing NVG during the night.

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<title>Objective assessment of simulated daytime and NVG image fidelity</title>

Cited by 6 publications

References 9 publications

Review of camouflage assessment techniques

Review of camouflage assessment techniques

Measuring the dynamics of camouflage in natural scenes using convolutional neural networks

NVG-the-Day: towards realistic night-vision training

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