Published April 30, 2025
| Version CC-BY-NC-ND 4.0
Journal article
Open
Evaluating the Effectiveness of Camouflage Patterns in Arid Environments
- 1. XUVI Technology Labs, Chennai (Tamil Nadu), India.
Description
Abstract: This research systematically evaluates the effectiveness of 14 distinct camouflage patterns across various arid environments. The study employs a comprehensive quantitative measurement framework, integrating two advanced image processing techniques: Gabor filters and Local Binary Patterns (LBP). These techniques provide an objective analysis of camouflage concealment by assessing the patterns' ability to blend into a diverse range of arid environmental backdrops. The research emphasizes the structural and textural aspects of camouflage patterns while deliberately excluding the influence of color palettes to isolate the impact of design elements. Data is collected and analyzed to quantify the performance of each pattern under controlled conditions, ensuring consistent and replicable results. The findings offer valuable insights into optimizing camouflage design, with practical implications for enhancing concealment strategies in military operations and wildlife research. By focusing on pattern design alone, this study contributes to a more nuanced understanding of how texture and structure influence the effectiveness of visual camouflage in arid landscapes.
Files
C103605030425.pdf
Files
(329.6 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:33f12434466e12c32b405d2fc5e2b919
|
329.6 kB | Preview Download |
Additional details
Identifiers
- DOI
- 10.54105/ijipr.C1036.05030425
- EISSN
- 2582-8037
Dates
- Accepted
-
2025-04-15Manuscript received on 07 March 2025 | First Revised Manuscript received on 15 March 2025 | Second Revised Manuscript received on 23 March 2025 | Manuscript Accepted on 15 April 2025 | Manuscript published on 30 April 2025.
References
- Cuthill, I.C., Stevens, M., Sheppard, J., Maddocks, T., Párraga, C.A. and Troscianko, T.S., 2005. Disruptive coloration and background pattern matching. Nature, 434(7029), pp.72-74. DOI: https://doi.org/10.1038/nature03312
- Mäenpää, T. and Pietikäinen, M., 2005. Texture analysis with local binary patterns. In Handbook of pattern recognition and computer vision (pp. 197-216). DOI: https://doi.org/10.1142/9789812775320_0011
- Stevens, M. and Merilaita, S. eds., 2011. Animal camouflage: mechanisms and function. Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511852053.001
- Behrens, R.R., 2002. False colors: art, design and modern camouflage. Bobolink Books. https://scholarworks.uni.edu/facbook/430/
- Mortlock, R.F., 2020. Camouflage Combat Uniform. Defense AR Journal, 27(4), pp.354-397. https://calhoun.nps.edu/bitstream/handle/10945/69407/Mortlock_camo uflage%20combat.pdf?sequence=1
- Denning, R.J., 2018. Camouflage fabrics. In Engineering of highperformance textiles (pp. 349-375). Woodhead Publishing. DOI: https://doi.org/10.1016/B978-0-08-101273-4.00016-0
- Anitole, G. & Johnson, R.L., (1989). Evaluation of Desert Camouflage Uniforms by Ground Observers. U.S. Army Belvoir Research, Development and Engineering Center, Fort Belvoir. https://apps.dtic.mil/sti/tr/pdf/ADA289273.pdf
- Dugas, A., Zupkofska, K.J., DiChiara, A., & Kramer, F.M. (2004). Universal camouflage for the future warrior. U.S. Army Research, Development and Engineering Command, Natick Soldier Center, Natick, MA. https://milspecmonkey.com/articles/acu/dugas.pdf
- Scaturro, S., 2011. From combat to couture: Camouflage in fashion (Doctoral dissertation, Fashion Institute of Technology. Fashion and Textile Studies). DOI: http://dx.doi.org/10.13140/RG.2.2.36233.03682
- King, A., 2014. The digital revolution: Camouflage in the twenty-first century. Millennium, 42(2), pp.397-424. DOI: https://doi.org/10.1177/0305829813512885
- Mortlock, R.F., 2020. Camouflage Combat Uniform. Defense AR Journal, 27(4), pp.354-397. https://calhoun.nps.edu/bitstream/handle/10945/69407/Mortlock_camo uflage%20combat.pdf?sequence=1
- Langhorne, J.L., Martinez, O.A., & Khilji, A., (2018). Standardized U.S.-led coalition forces uniform. Naval Postgraduate School, Monterey, California. https://apps.dtic.mil/sti/trecms/pdf/AD1059969.pdf
- Gonzalez, R.C., 2009. Digital image processing. Pearson education india. https://dl.icdst.org/pdfs/files4/01c56e081202b62bd7d3b4f8545775fb.p df
- Jain, A.K. and Farrokhnia, F., 1991. Unsupervised texture segmentation using Gabor filters. Pattern recognition, 24(12), pp.1167-1186. DOI: https://doi.org/10.1016/0031-3203(91)90143-S
- Mehrotra, R., Namuduri, K.R. and Ranganathan, N., 1992. Gabor filterbased edge detection. Pattern recognition, 25(12), pp.1479-1494. DOI: https://doi.org/10.1016/0031-3203(92)90121-X
- Kamarainen, J.K., Kyrki, V. and Kalviainen, H., 2006. Invariance properties of Gabor filter-based features-overview and applications. IEEE Transactions on image processing, 15(5), pp.1088-1099. DOI: https://doi.org/10.1109/TIP.2005.864174
- Lindahl, T., 2007. Study of local binary patterns. https://www.divaportal.org/smash/get/diva2:23908/FULLTEXT01.pdf
- Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T., Pietikäinen, M., Hadid, A., Zhao, G. and Ahonen, T., 2011. Local binary patterns for still images. Computer vision using local binary patterns, pp.13-47. DOI: https://doi.org/10.1007/978-0-85729-748-8
- Merilaita, S. and Stevens, M., 2011. Crypsis through background matching. Animal camouflage: mechanisms and function, pp.17-33. DOI: https://doi.org/10.1017/CBO9780511852053.002
- Thepade, S. D., Awhad, R., & Khandelwal, P. (2020). Image Retrieval with Fusion of T hepade's Sorted B lock Truncation Codingnary based Colo r and Local Binary Pattern based Texture Features with Different Color Places. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 5, pp. 28–34). DOI: https://doi.org/10.35940/ijitee.e1963.039520
- Tamilselvi, M., & Karthikeyan, S. (2019). A Face Recognition System Using Directional Binary Code Algorithm And Multi-Svm. In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6s3, pp. 1201–1208). DOI: https://doi.org/10.35940/ijeat.f1204.0986s319
- Singh, A., & Jindal, K. (2019). Realization of Image Processing Algorithm based on FPGA. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 2882–2885). DOI: https://doi.org/10.35940/ijrte.c5338.098319
- Dutta, D., Halder, T., Penchala, A., Krishna, K. V., Prashnath, G., & Chakravarty, D. (2024). A Case Study on Image Co-Registration of Hyper Spectral and Dual (L & S) Band SAR Data and Ore Findings Over Zewar Mines, India. In International Journal of Emerging Science and Engineering (Vol. 12, Issue 6, pp. 17–25). DOI: https://doi.org/10.35940/ijese.a8055.12060524
- S, D., & P L, L. (2020). Binary Class Classification of Software Faults in Software Modules using Popular Machine Learning Techniques. In International Journal of Innovative Science and Modern Engineering (Vol. 6, Issue 6, pp. 14–18). DOI: https://doi.org/10.35940/ijisme.f1221.046620