Abstract:

In the process of camouflage design, information of background images is classified, and background
spots acquired using image segmentation. Camouflage patterns corresponding to luminance and natural textures of the background are designed. The common method of mean clustering uses the gray information for image segmentation, resulting in fuzzy details of textures. This paper proposes a new method based on self organizing feature map (SOFM) neural-networks for clustering segmentation. The method uses the entire image as input to the neural-networks, and colors after segmentation as output. Calculation is done iteratively using its special functions in the learning process of self-organizing feature map neural-networks until the learning stops. Experiments show that the SOFM method can preserve more texture details than the mean-clustering method and provide good results.

Key words: camouflage design, image segmentation, mean clustering, self organizing feature map (SOFM)