Camouflage Design of Analysis Based on HSV Color Statistics and K-means Clustering

TL;DR

This paper proposes an objective method for evaluating camouflage effectiveness using HSV color statistics and K-means clustering to analyze image similarity based on color and texture features.

Contribution

It introduces a novel approach combining HSV color analysis and K-means clustering for objective camouflage evaluation, moving beyond subjective assessments.

Findings

Effective camouflage pattern evaluation using image similarity metrics.

HSV color model and K-means clustering improve analysis accuracy.

Provides a quantitative framework for camouflage design assessment.

Abstract

Since ancient times, it has been essential to adopting camouflage on the battlefield, whether it is in the forefront, in-depth or the rear. The traditional evaluation method is made up of people opinion. By watching target or looking at the pictures, and determine the effect of camouflage, so it can be more influenced by man's subjective factors. And now, in order to objectively reflect the camouflage effect, we set up a model through using images similarity to evaluate camouflage effect. Image similarity comparison is divided into two main image feature comparison: image color features and texture features of images. We now using computer design camouflage, camouflage pattern design is divided into two aspects of design color and design plaques. For the design of the color, we based on HSV color model, and as for the design of plague, the key steps are the background color edge extraction, we adopt algorithm based on k-means clustering analysis of the method of background color edge extraction.