Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Camouflage detection with texture statistical characterization in autonomous systems

Camouflage detection with texture statistical characterization in autonomous systems The purpose of the research work is to detect camouflaged objects in autonomous systems of military applications and civilian applications such as detecting insects in paddy fields, identifying duplicate products in different texture environments.Design/methodology/approachCamouflaged objects detection is performed by smoothing texture with nonlinear models and characterizing with statistical methods to detect the objects.FindingsThere are few challenges in existing camouflaged objects detection due to the complexities involved in the detection process. This work proposes a constructive approach with texture statistical characterization for camouflage detection. The proposed technique is found to be better than existing methods while assessing its performance using precision and recall.Research limitations/implicationsEven though there is lot of research work carried, there are few challenges for autonomous systems in camouflage detection due to the complexities involved in the detection process such as texture modeling and dynamic background problems and environment conditions for autonomous system.Practical implicationsCamouflage detection finds potential applications in security systems, surveillance, military and autonomous systems. The proposed work is implemented in different environments for camouflage detection.Social implicationsSocial problems such as image acquisition environment, time of day, desert, forest and grass fields of paddy.Originality/valueThe proposed method detects camouflaged objects in autonomous systems where it is applied for images of different kinds. It is found to be effective on images recorded in battlefield and challenging environments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Unmanned Systems Emerald Publishing

Camouflage detection with texture statistical characterization in autonomous systems

Download PDF
11 pages

Loading next page...
 
/lp/emerald-publishing/camouflage-detection-with-texture-statistical-characterization-in-8VdJnKORm9

References (28)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2049-6427
DOI
10.1108/ijius-12-2019-0073
Publisher site
See Article on Publisher Site

Abstract

The purpose of the research work is to detect camouflaged objects in autonomous systems of military applications and civilian applications such as detecting insects in paddy fields, identifying duplicate products in different texture environments.Design/methodology/approachCamouflaged objects detection is performed by smoothing texture with nonlinear models and characterizing with statistical methods to detect the objects.FindingsThere are few challenges in existing camouflaged objects detection due to the complexities involved in the detection process. This work proposes a constructive approach with texture statistical characterization for camouflage detection. The proposed technique is found to be better than existing methods while assessing its performance using precision and recall.Research limitations/implicationsEven though there is lot of research work carried, there are few challenges for autonomous systems in camouflage detection due to the complexities involved in the detection process such as texture modeling and dynamic background problems and environment conditions for autonomous system.Practical implicationsCamouflage detection finds potential applications in security systems, surveillance, military and autonomous systems. The proposed work is implemented in different environments for camouflage detection.Social implicationsSocial problems such as image acquisition environment, time of day, desert, forest and grass fields of paddy.Originality/valueThe proposed method detects camouflaged objects in autonomous systems where it is applied for images of different kinds. It is found to be effective on images recorded in battlefield and challenging environments.

Journal

International Journal of Intelligent Unmanned Systems – Emerald Publishing

Published: Dec 31, 2020

Keywords: Camouflage detection; Texture analysis; Gray-level co-occurrence matrix (GLCM); Normalized co-occurrence matrix (NCM); Statistical modeling

There are no references for this article.