Imitation camouflage synthesis based on shallow neural network

Deep learning technology has been widely used in the military field, which have achieved great success. The traditional method for painting camouflage either using the background information or the artificial pattern. None of the traditional methods can both consider the background information and camouflage rules. In this paper, a new automatic camouflage generation framework is proposed. A method for generating camouflage pattern is designed. The imitation camouflage pattern is synthesized from the features of both background and artificial pattern. In our method, the texture feature of both background and traditional pattern patches are extracted from the feature maps of shallow neural network (SNN). Based on the feature maps, statistic information of second order differential and mean subtracted contrast normalized coefficients for texture and color is extracted. By iterating to optimize the imitation camouflage to be generated, the statistical information of the imitation camouflage can approximate the characteristic statistical information of the background and pattern. The new generated camouflage pattern can contain the color and texture information of background; besides, it can maintain the traditional patch camouflage criteria. Our approach makes camouflage painting more flexible and allows the target to better infuse into the background. And our method is designed for the preparation of painting camouflage.

Automated summary

In this paper, a new automatic camouflage generation framework is proposed. The method can extract features from both background and traditional camouflage patterns, and optimize the generated camouflage to approximate the statistical information of the background and pattern. The new camouflage pattern contains color and texture information of the background while meeting the traditional camouflage criteria.