基于cnn的伪装图案自动设计

Xinjian Wei, Kaidi Wang, Guangxu Li, Hyoungseop Kim
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引用次数: 2

摘要

为了快速适应战场环境的变化,军用迷彩必须具有变异性。如果衣服和坦克等车辆的伪装图案与环境不同,敌人的摄像机就很容易发现。我们都知道,同样的模式被用于当今世界上大多数的军事。本文提出了一种基于卷积神经网络的图像特征提取方法。然后将图案与环境风格图案相结合。最后将合成图像映射到实际的3D服装和车辆表面。本文采用眼动仪对结果进行评价,以便更好地进行比较。即使在不同复杂的环境中,我们也能做出合适的图案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Automatic Design of Camouflage Patterns Based on CNNs
In order to get with the environmental changes of battlefield quickly, the military camouflage should be changeable. If the camouflage patterns of the clothes and vehicles like tanks are different from the environment, it's very easy for cameras of enemies to find. As we all know that the same patterns is used in the most of current military all over the world. In this paper, we propose a novel feature-extraction method from an image using convolutional neural networks. Then the pattern will be combined with the environmental style pattern. The composite image is mapped onto the surface of the actual 3D clothes and vehicles finally. In this paper, the Eye-Movement equipment is applied to evaluate the results for better comparison. We can produce the proper pattern even the different and complicated environment.
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