Assessment of Camouflage Effectiveness Based on Perceived Color Difference and Gradient Magnitude

Bai, Xueqiong; Liao, Ningfang; Wu, Weiming

doi:10.3390/s20174672

Cited by 14 publications

(2 citation statements)

References 27 publications

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“…To implement our idea, different patterns information is needed. Five different sample CPs used in other studies 25,26 were selected. In fact, the selected patterns are widely used CPs that are representative of what is utilized by army forces in the field.…”

Section: Pattern Collectionmentioning

confidence: 99%

Hybrid camouflage pattern generation using neural style transfer method

Daneshvar

Tehran

Zhang

2021

Color Research & Application

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Manipulating existing camouflage patterns is a challenging issue in the process of camouflage pattern design. In this article, we present an effective approach based on the neural style transfer method to generate a hybrid camouflage pattern by manipulating two given camouflage patterns. Using a convolutional network trained on image recognition, content and style are represented by the correlations between feature maps in several layers of the network. In this regard, we utilized different commonly used camouflage patterns as content and style images. Then, by performing the style transfer algorithm on selected camouflage patterns, a new hybrid camouflage pattern was generated. The hybrid pattern inherits the appearance features of the content and the style images. Also, the colors of the hybrid pattern were controlled by carefully selecting the input colors. It was concluded that the proposed method is useful for adding or deleting some features of an existing camouflage pattern. In fact, it is a professional tool for pixelating, depixelating, blurring, and gradual coloring of camouflage patterns. Furthermore, we demonstrate the effectiveness of hybrid camouflage patterns using visual assessment. The results of the subjective assessment show that the proposed method is efficient for generating successful camouflage patterns.

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Section: Pattern Collectionmentioning

confidence: 99%

Hybrid camouflage pattern generation using neural style transfer method

Daneshvar

Tehran

Zhang

2021

Color Research & Application

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“…Therefore, there's a growing demand for computational models capable of determining camouflage effectiveness using digital images.. Image based camouflage effectiveness assessment algorithms compute object saliency using digital object-background contrast metrics [1,17,18]. These metrics have frequently little or no relation to human visual perception.…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network as a Potential Tool for Camouflage Assessment

Van der Burg,

Toet,

Perone

et al. 2024

Preprint

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Camouflage evaluation typically involves human visual search and detection experiments that are time-consuming and expensive. Hence, there is a need for models that compute camouflage effectiveness from digital imagery. Convolutional neural networks are a powerful tool for automatic object detection and recognition. We investigated whether such a network (YOLO) can also provide a measure of camouflage effectiveness that is related to human perception. To this end, human performance measures of camouflage effectiveness such as detection time and target conspicuity were obtained in observer experiments and compared with the performance of the (standard, i.e. pre-trained) YOLO-V4-tiny algorithm. YOLO provides the probability that a detected object is correctly classified, and this is adopted as our measure of camouflage effectiveness. We compared the YOLO-predicted classification probability for a person in camouflage clothing moving through rural and urban scenes to human detection performance. Overall, camouflage effectiveness predicted by YOLO correlates with human observer performance. At close distances, YOLO’s classification performance appears sensitive to high-contrast shape-breaking elements of a camouflage pattern. This suggests that YOLO may be adapted to assess camouflage effectiveness for a wide range of applications, such as evaluating or optimizing one’s signature and predicting optimal hiding locations in a given environment. Further research is required to fully establish YOLO’s limitations and applicability for this purpose.

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