Abstract

Camouflage plays a crucial role in countering reconnaissance and concealing military objects. In the defense industry, camouflage techniques, such as camouflage nets and patterns, aim to minimize detectability by reducing distinctive features. However, existing evaluation methodologies face challenges, including inconsistent visual perception and incomplete evaluation indices. This study proposes a comprehensive similarity-based approach for assessing camouflage effectiveness using both objective and subjective evaluation methods. The objective evaluation quantifies camouflage similarity by analyzing four key indices: image color, brightness, texture, and structure. These are represented as the color similarity index (S_C), luminance similarity index (S_L), structure similarity index (S_S), and texture similarity index (S_T). The entropy weighting method (EWM) is employed to optimize feature weighting and extract meaningful information. The subjective evaluation assesses detection time and perceived similarity based on a user study involving 20 participants across four different camouflage scenes. Results indicate that the comprehensive similarity model outperforms conventional evaluation methods, demonstrating superior predictive accuracy. Among the indices, S_C plays a dominant role, highlighting the significant impact of color on human visual perception. This study demonstrates that a visual perception-based approach enhances camouflage evaluation accuracy and provides a robust, reliable framework for assessing camouflage performance.

Keywords

Camouflage effectiveness, Similarity index, Objective evaluation, Subjective evaluation, Feature vector, Visual perception.

References

[1] Pulla RC, Guruva RA, Rama RCB. Camouflaged object detection for machine vision applications. International Journal of Speech Technology. 2020; 23(2):327-35.

[2] Gan Y, Liu C, Li H, Wang B, Ma S, Liu Z. An evaluation method of dynamic camouflage effect based on multifeature constraints. IEEE Access. 2020; 8:193845-55.

[3] Bai X, Liao N, Wu W. Assessment of camouflage effectiveness based on perceived color difference and gradient magnitude. Sensors. 2020; 20(17):1-10.

[4] Cheng XP, Zhao DP, Yu ZJ, Zhang JH, Bian JT, Yu DB. Effectiveness evaluation of infrared camouflage using image saliency. Infrared Physics & Technology. 2018; 95:213-21.

[5] Lin CJ, Chang CC, Liu BS. Developing and evaluating a target-background similarity metric for camouflage detection. PLoS One. 2014; 9(2):1-11.

[6] Patil KV, Pawar KN. Method for improving camouflage image quality using texture analysis. International Journal of Computer Applciations. 2017; 180(8):6-8.

[7] Hecker R. Camaeleon-camouflage assessment by evaluation of local energy, spatialfrequency, and orientation. In characterization, propagation, and simulation of sources and backgrounds II 1992 (pp. 342-9). SPIE.

[8] Lin W, Chen Yh, Wang Jy, Su Rh, Yu Sl. Camouflage assessment method based on image features and psychological perception quantity. Acta Armamentarii. 2013; 34(4):412-17.

[9] Wang Z, Yan YH, Jiao XY. Multi-index comprehensive evaluation of camouflage based on gray theory. Acta Armamentarii. 2013; 34(10):1250-7.

[10] Rong X, Jia Q, Xu W, Lv X, Hu J. Camouflage effect evaluation of pattern painting based on moving object detection. In international conference on energy, power and electrical engineering 2016 (pp. 244-7). Atlantis Press.

[11] Song J, Liu L, Huang W, Li Y, Chen X, Zhang Z. Target detection via HSV color model and edge gradient information in infrared and visible image sequences under complicated background. Optical and Quantum Electronics. 2018; 50:1-3.

[12] Yuan X, Lv X, Li L, Wang X, Zhang Z. Image feature extraction based on the camouflage effectiveness evaluation. Proceedings of the 2nd International Conference on Advances in Materials, Machinery, Electronics 2018 (pp. 1-6). AIP.

[13] Toet A, Hogervorst MA. Urban camouflage assessment through visual search and computational saliency. Optical Engineering. 2013; 52(4).

[14] Xue F, Yong C, Xu S, Dong H, Luo Y, Jia W. Camouflage performance analysis and evaluation framework based on features fusion. Multimedia Tools and Applications. 2016; 75:4065-82.

[15] Wang J, Xu W, Qu Y, Cui G. Research on measurement method of optical camouflage effect of moving object. In optical measurement technology and instrumentation 2016 (pp. 821-8). SPIE.

[16] Wang Z, Bovik AC. A universal image quality index. IEEE Signal Processing Letters. 2002; 9(3):81-4.

[17] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing. 2004; 13(4):600-12.

[18] Lin CJ, Prasetyo YT, Siswanto ND, Jiang BC. Optimization of color design for military camouflage in CIELAB color space. Color Research & Application. 2019; 44(3):367-80.

[19] Volonakis TN, Matthews OE, Liggins E, Baddeley RJ, Scott-samuel NE, Cuthill IC. Camouflage assessment: machine and human. Computers in Industry. 2018; 99:173-82.

[20] Hogervorst MA, Toet A, Jacobs P. Design and evaluation of (urban) camouflage. In infrared imaging systems: design, analysis, modeling, and testing XXI 2010 (pp. 30-40). SPIE.

[21] Xue W, Zhang L, Mou X, Bovik AC. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Transactions on Image Processing. 2013; 23(2):684-95.

[22] Lin CJ, Chang CC, Lee YH. Developing a similarity index for static camouflaged target detection. The Imaging Science Journal. 2014; 62(6):337-41.

[23] Li Y, Liao N, Deng C, Li Y, Fan Q. Assessment method for camouflage performance based on visual perception. Optics and Lasers in Engineering. 2022; 158:107152.

[24] He Z, Gan Y, Ma S, Liu C, Liu Z. Evaluation method for the hyperspectral image camouflage effect based on multifeature description and grayscale clustering. EURASIP Journal on Advances in Signal Processing. 2023; 2023(1):1-16.

[25] Li N, Li L, Jiao J, Xu W, Qi W, Yan X. Research status and development trend of image camouflage effect evaluation. Multimedia Tools and Applications. 2022; 81(21):29939-53.

[26] Juntang Y, Weidong X, Qingkai Q, Yang Q. Research on camouflage effect evaluation method of moving object based on video. In proceedings of the 15th SIGGRAPH conference on virtual-reality continuum and its applications in industry 2016 (pp. 441-6). ACM.

[27] Li C, Li Z, Wang Z, Xu Y, Luo MR, Cui G, et al. Comprehensive colour solutions: CAM16, CAT16, and CAM16‐UCS. Colour Research & Application. 2017; 42(6):703-18.

[28] Moroney N, Fairchild M, Hunt R, Li C. The CIECAM02 color appearance model. Rochester Institute of Technology, Digital Institutional Repository. 2002.

[29] Yang X, Xu WD, Jia Q, Liu J. MF-CFI: a fused evaluation index for camouflage patterns based on human visual perception. Defence Technology. 2021; 17(5):1602-8.

[30] Yang X, Xu W, Jia Q. A dynamic camouflage effect evaluation method based on feature statistics. Acta Armamentarii. 2019; 40(8):1693-9.

[31] Xin Y, Weidong X, Lei X, Wannian Z, Jiyao T. A camouflage effect detection model for fixed targets. In journal of physics: conference series 2019 (pp. 1-7). IOP Publishing.

[32] Ma S, Liu C, Li H, Wang H, He Z. Camouflage effect evaluation based on hyperspectral image detection and visual perception. Acta Armamentarii. 2019; 40(7):1485.

[33] Yu Z, Xue L, Xu W, Liu J, Jia Q, Hu J, et al. Assessing target optical camouflage effects using brain functional networks: a feasibility study. Defence Technology. 2024; 34:69-77.

[34] Zhou X, Zhu W, Liu F, Yang W, Chu M. The evaluation of camouflage based on image edge contour similarity. In 7th international conference on communication, image and signal processing 2022 (pp. 178-82). IEEE.

[35] Gupta P, Srivastava P, Bhardwaj S, Bhateja V. A modified PSNR metric based on HVS for quality assessment of color images. In international conference on communication and industrial application 2011 (pp. 1-4). IEEE.

[36] Piella G, Heijmans H. A new quality metric for image fusion. In proceedings international conference on image processing (Cat. No. 03CH37429) 2003 (pp. III-173). IEEE.

[37] Amintoosi M, Fathy M, Mozayani N. Video enhancement through image registration based on structural similarity. The Imaging Science Journal. 2011; 59(4):238-50.

[38] Song L, Geng W. A new camouflage texture evaluation method based on WSSIM and nature image features. In international conference on multimedia technology 2010 (pp. 1-4). IEEE.

[39] Cheng XP, Shu BW, Chang YJ, Li X, Yu DB. Evaluation of infrared camouflage effectiveness via a multi-fractal method. Defence Technology. 2021; 17(3):748-54.

[40] Ying JJ, Wu DS, Zhou B, Chen YD, Huang FY. Dynamic infrared target camouflage effect evaluation. In fifth symposium on novel optoelectronic detection technology and application 2019 (pp. 155-63). SPIE.

[41] Kataoka S, Kikuchi H, Huttunen H, Hwang J, Muramatsu S, Shin J. Color-tone similarity evaluation in image quality assessment. In ITC-CSCC conference, Yeosu, Korea 2013 (pp. 639-42).

[42] Qin J, Qu L, Zhu L, Hu J, Song S. Optical camouflage effect objective evaluation method research under the condition of complex backgrounds. In MATEC web of conferences 2016 (pp. 1-4). EDP Sciences.

[43] Johnson GM, Fairchild MD. A top down description of S‐CIELAB and CIEDE2000. Color Research & Application. 2003; 28(6):425-35.

[44] Kwak Y, Macdonald L. Characterisation of a desktop LCD projector. Displays. 2000; 21(5):179-94.

[45] Poirson AB, Wandell BA. Appearance of colored patterns: pattern–color separability. Journal of the Optical Society of America A. 1993; 10(12):2458-70.

[46] Zhang L, Zhang L, Mou X, Zhang D. FSIM: a feature similarity index for image quality assessment. IEEE transactions on Image Processing. 2011; 20(8):2378-86.

[47] Sara U, Akter M, Uddin MS. Image quality assessment through FSIM, SSIM, MSE and PSNR-a comparative study. Journal of Computer and Communications. 2019; 7(3):8-18.

[48] Rehman A, Wang Z. Reduced-reference image quality assessment by structural similarity estimation. IEEE Transactions on Image Processing. 2012; 21(8):3378-89.

[49] Renieblas GP, Nogués AT, González AM, Gómez-leon N, Del CEG. Structural similarity index family for image quality assessment in radiological images. Journal of Medical Imaging. 2017; 4(3).

[50] Xu WD, Wang XW. Camouflage detection and evaluation theory and technology. Washington, DC, USA: National Defense University Press, 2015: 78–80.

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