Digital Camouflage Pattern Generation Approach Based on Fragmentation Edges

This paper demonstrates a camouflage pattern generation approach based on fragmentation edges. The proposed camouflage pattern generation method features the K-means algorithm improved by the sparrow search algorithm (SSA-Kmeans) to extract the background dominant colors, and background pattern segmentation using the simple linear iterative clustering with random matrix algorithm according to the fragmentation weight. Compared with conventional methods, namely the watershed segmentation algorithm and the recursive overlapping of pattern templates algorithm, the proposed adaptive camouflage pattern generation approach shows obvious advantages in texture camouflage effect, and the difference is reduced by more than 64% compared with the other two algorithms.

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Publication Details

Published
2025-04-18
DOI
https://doi.org/10.1109/eeice65049.2025.11034215
Primary Topic
Visual Attention and Saliency Detection
Type
article
Field-Weighted Citation Impact
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article

Digital Camouflage Pattern Generation Approach Based on Fragmentation Edges

Congwei Liao, Lianwen Deng, Mingjun Li, Liqiu Li
Visual Attention and Saliency Detection
article

Digital Camouflage Pattern Generation Approach Based on Fragmentation Edges

Congwei Liao, Lianwen Deng, Mingjun Li, Liqiu Li
article en

Abstract

This paper demonstrates a camouflage pattern generation approach based on fragmentation edges. The proposed camouflage pattern generation method features the K-means algorithm improved by the sparrow search algorithm (SSA-Kmeans) to extract the background dominant colors, and background pattern segmentation using the simple linear iterative clustering with random matrix algorithm according to the fragmentation weight. Compared with conventional methods, namely the watershed segmentation algorithm and the recursive overlapping of pattern templates algorithm, the proposed adaptive camouflage pattern generation approach shows obvious advantages in texture camouflage effect, and the difference is reduced by more than 64% compared with the other two algorithms.

Central South University (CN), Shenzhen Technology University (CN)
Openalex Percentile: Top 88%
Visual Attention and Saliency Detection
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