Multi-criteria evaluation method applied in the field of camouflage
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https://doi.org/10.54939/1859-1043.j.mst.FEE.2022.154-163Keywords:
Visual camouflage; Pattern; Background; TOPSIS algorithm; Matlab.Abstract
Camouflage has a long history in the development of mankind and has developed rapidly since the beginning of the twentieth century along with electronic and optical technology. In Vietnam, in addition to the traditional approach, modern camouflage has attracted researchers inside and outside the military. However, the evaluation of camouflage effectiveness is still in the early stages with the lack of official criteria and references. In this article, we propose a method to evaluate the effectiveness of camouflage in the visible light spectrum using a multi-criteria algorithm, combined with a visual evaluation method by human eyes to compare and evaluate. The computer simulation results show that the multi-criteria evaluation method has high reliability, giving similar results to the visual evaluation method.
References
[1]. J. R. Rao, “Introduction to camouflage and deception”, Defence research & development organisation, Ministry of Defence, New Delhi, (1999).
[2]. V. H. Khánh, Báo cáo tổng kết đề tài Viện KH&CN quân sự, “Nghiên cứu phương pháp và phương tiện để xây dựng cơ sở dữ liệu phục vụ công tác ngụy trang phòng chống các phương tiện trinh sát ảnh”, (2021).
[3]. Joseph E. Peak, Thomas Honke, “Guidelines for Camouflage Assessment Using Observers”, RTO AGARDograph, (2006).
[4]. C. L. Lin, C. C. Chang and Y. H. Lee, “Developing a similarity index for static camouflaged target detection”, The Imaging Science Journal, No. 6, Vol. 62, (2014). DOI: https://doi.org/10.1179/1743131X13Y.0000000057
[5]. C. L. Hwang, K. Yoon, “Multiple Attribute Decision Making: Methods and Applications”, New York: Springer-Verlag (1981). DOI: https://doi.org/10.1007/978-3-642-48318-9
[6]. R. Schoene, J. Meidow, E. Mauer, “Feature evaluation for target/background discrimination in image sequence taken by approaching sensors,” Proc. SPIE 7697, Signal Processing, Sensor Fusion, and target Recognition XIX, 769718 (2010). DOI: https://doi.org/10.1117/12.850190
[7]. Y. M. Liu, J. Y. Wang, W. Liu, S. D. Yuan, “Analysis of methods of detection and evaluation of optical camouflage,” Aerospace Electronic Warfare, Papers 26 (1), 42-44 (2010).
[8]. D. Wang, X. L. Lv, W. D. Xu, Y. L. Pan, W. Lin, “Camouflage Application Models With Pixel Frequency Analysis, ” Journal of PLA University of Science and Technology (Natural Science Edition), Papers 5(3), 74-77 (2004).
[9]. H. Wang, T. Z. Bai, “Evaluation of Optical Camouflage Based on Tolerance Nearness Sets Theory,” Acta Optica Sinica, Papers 32(12), 128-133(2012). DOI: https://doi.org/10.3788/AOS201232.1210001
[10]. P. Y. Wang, D. H. Zhao, M. H. Li, “Optical Camouflage Effect Assessment Based on Digital Image Inpainting Technology,” Laser & Optoelectronics Progress, Papers 55(3), 253-259(2018). DOI: https://doi.org/10.3788/LOP55.031011
[11]. J. Yu, L. F. Zhu, H. L. Du, “Evaluation Model of Optical Camouflage Effect Based on BP Neural Network,” Shipboard Electronic Countermeasure, Papers 32(6), 55-57(2009).
[12]. K. Wang, Z. Liu, Y. Wang, S. Luo, “Evaluation of optical camouflage effect based on entropy weight TOPSIS method,” Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 118481N, 1 June (2021); doi: 10.1117/12.2600206. DOI: https://doi.org/10.1117/12.2600206
[13]. Z. Huang, J. Leng, “Analysis of Hu’s Moment Invariants on Image Scaling and Rotation,” International Conference on Computer Engineering and Technology, V7, (2010).
