RETRACTED: Camouflage texture design based on its camouflage performance evaluation

Xue, Feng; Wu, Fan; Wang, Jianwei; Hu, Yue

doi:10.1016/j.neucom.2016.07.081

Cited by 22 publications

(8 citation statements)

References 12 publications

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“…Recent evaluation studies agree with these previous findings, for instance that there is no clear relation between the output of the CAMAELEON signature metric and observer ratings for camouflaged targets [32,34]. Although several new methods have been introduced since the conclusion of this study (e.g., [10,11,61,[166][167][168]204,206,207]) computational signature analysis methods still do not fully represent the range of significant visual and cognitive processes driving target acquisition performance [34]. Some promising methods that appear to reliably predict human visual detection of camouflaged targets are target-background similarity metrics like Structural Similarity (SSIM: [133]), the Universal Image Quality Index (UIQI: [11]), and the Gabor Edge Disruption Ratio (GabRat: [61]).…”

Section: Local Clutter Metricssupporting

confidence: 89%

“…Recently saliency algorithms have been extended to produce spatio-temporal saliency maps for the analysis of dynamic imagery [142,155,[158][159][160][161][162][163][164][165]. Other recent methods combine saliency maps with other images features like local regularity [166], entropy [167] or linear features [168]. Only a few saliency algorithms have been validated with observer data [129,130].…”

Section: Saliency Modelsmentioning

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: Local Clutter Metricssupporting

confidence: 89%

Section: Saliency Modelsmentioning

confidence: 99%

Review of camouflage assessment techniques

Toet

Hogervorst

2020

Target and Background Signatures VI

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“…As shown in Figure 1A, the width and the length of one repetition were 63 cm (1512 pixels) and 62.5 cm (1500 pixels) respectively. Different from some previous camouflage studies which applied digital camouflage 19,[26][27][28][29][30][31][32][33][34][35][36][37] , an existing camouflage was captured by a Canon EOS 5D Mark II. In addition, an X-Rite ColorChecker Color Rendition Chart was used to create an ICC profile to be used in the experiment.…”

Section: Camouflage Collectionmentioning

confidence: 99%

Optimization of color design for military camouflage in CIELAB color space

Lin

Prasetyo

Siswanto

et al. 2019

Color Research & Application

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The assessment of military camouflage is a key consideration in the modern military field. Traditionally, the assessment relies on traditional human visual detection tests because a large scale multi‐level and multi‐factor experiments are time‐ and resource‐consuming. One aspect of camouflage assessment, to which this current study pertains, entails improving upon or “enhancing” an existing or “selected” design. The current study presents a new and practical approach for enhancing the selected military camouflage by utilizing response surface methodology (RSM) of %L*, %a*, and %b* in CIELAB color space. Ten participants were recruited to evaluate 35 variations of %L*, %a*, and %b* on camouflage similarity index (CSI) and reaction time (RT). Based on RSM, the optimum combination occurs at L*: 61.4966, a*: −5.6505, and b*: 10.5114. In addition, a predictive algorithm to calculate the optimum shift of %L*, %a*, and %b* from the original camouflage to the improved camouflage derived from RSM is also proposed. The optimum shift occurs at −25%L*, −55%a*, and + 80%b*. In the end, a new design guideline is proposed for the enhancement of selected military camouflage, which adopts the present study's research findings.

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“…The design of special camouflage patterns tailored to individual regions, initially based on the Digital Colour Management System (DCMS), has already been tested in various environments, 2 and has allowed for selection of the dominating colours of the camouflage texture in a semi‐automatic manner, with active participation of the DCMS operator. Research illustrating the benefits of using this particular method was published by Assis et al 3 The striving towards automatic generation of camouflage texture is therefore focused on the statistic and probabilistic methods of processing, and generating images 3 which would eliminate the participation of the human factor from the camouflage pattern generation process 4,5 …”

Section: Introductionmentioning

confidence: 99%

Colour management system: Monte Carlo implementation for camouflage pattern generation

Przybył

Radosz

Januszko

2020

Coloration Technology

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The Monte Carlo method provides a simple yet powerful and flexible tool for simulation of complex or random systems. This paper contains an overview of the theory behind the method and some simple examples to illustrate its implementation to model the refinement of patterns on the camouflaged surface. The objective is to provide an introduction to the method and to present a useful tool for solving practical problems. The first attempts of its application for military camouflage pattern generation are presented. A colour management system utilising the Monte Carlo method and based on the imaging and spectral data enabled the design of a dedicated camouflage pattern for both small-and large-size military objects.

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RETRACTED: Camouflage texture design based on its camouflage performance evaluation

Cited by 22 publications

References 12 publications

Review of camouflage assessment techniques

Review of camouflage assessment techniques

Optimization of color design for military camouflage in CIELAB color space

Colour management system: Monte Carlo implementation for camouflage pattern generation

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