Combining Max pooling-Laplacian theory and k-means clustering for novel camouflage pattern design - Wan, Minhao; Zhao, Dehui; Zhao, Baogui (Frontiers in Neurorobotics, 2022)
Camouflage is the main means of anti-optical reconnaissance, and camouflage pattern design is an extremely important step in camouflage. Many scholars have proposed many camouflage pattern generation methods but fast and accurate camouflage pattern generation is still a difficult problem. In this paper, a new camouflage pattern generation method is designed independently, firstly, the Max Pooling theory combined with the discrete Laplacian differential operator is applied and the Max Pooling-Laplacian algorithm is proposed to compress and enhance the target background, in order to improve the accuracy and speed of camouflage pattern generation; combined with the K-means clustering principle, the background pixel primitives are used as the processing object, and the sample data are computed iteratively to obtain the camouflage pattern blended with the background. The results of the evaluation using color similarity and shape similarity show that the new camouflage pattern generation method achieves camouflage pattern design for different backgrounds and achieves good results.