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Proceedings of the International Conference on Industrial Engineering and Operations Management
Dubai, UAE, March 10-12, 2020
© IEOM Society International
Utilization of Color Similarity Index for Evaluating Existing
Military Camouflage Designs
Yogi Tri Prasetyo
School of Industrial Engineering and Engineering Management
Mapúa University
658 Muralla St., Intramuros, Manila 1002, Philippines
Abstract
Military camouflage is an important part of defense technology. It is designed to confuse the enemy by visually
merging the outline of military design to the surrounding environment. The purpose of this study was to apply a color
similarity index for evaluating existing military camouflages designs. Camouflage Similarity Index (CSI) was utilized
as a color similarity index and the value varies between 0 to 1. The best value of 0 is achieved if the selected existing
military camouflage design perfectly blends with the surrounding environment. 10 existing military camouflage
designs from different regions were evaluated under 14 different locations in the swamp environment. The results
indicated that the CSI was an effective tool for identifying the effectiveness of existing military camouflage designs
across regions. Interestingly, even the CSI values were found different among 10 selected designs, Post-hoc Tukey
HSD test revealed that there was statistical difference between each design and it could be categorized into 3 different
groups. This study contributed to the advancement of color similarity index to the existing military camouflage and the results would be very useful for military research organizations, ministry of defense, and textile engineer.
Keywords
Color Similarity Index, Military Camouflage, Camouflage Similarity Index, Defense Technology, Color Algorithm.
1. Introduction
Military camouflage is an important part of the army combat uniform. It is an attempt to minimize the difference
between the army combat uniform and the surrounding background so that human eyes and military detection
instruments struggle to detect and distinguish the target (Xue et al., 2016). Military research organizations and color
researchers are continually engaged in the test and evaluation of prospective camouflage patterns, seeking to maximize
concealment while also considering the background heterogeneity of diverse operational contexts (Brunye et al., 2017;
Chang et al., 2012; Lin et al., 2014c; Xue et al., 2016b; Xue et al., 2018). By advancing military camouflage research,
soldiers could improve the survivability and mission effectiveness by preventing visual observation and other military
sensors from detecting both the soldiers and their equipment (Killian & Hepfinger, 1992; Chang et al., 2012;
Fincannon et al., 2013).
Military camouflage effectiveness is often assessed by image quality assessment algorithm. Previously, Zhang et al
(2013) proposed an algorithm based on Fourier transform and Gaussian low-pass filter (LPF) to mix the color based
on tricolor angular frequencies. In the model, the tricolor angular frequencies were introduced to the spatial frequency
response function of human color vision, and the effects of atmospheric attenuation and air screen brightness on color
mixture were also considered (Zhang et al., 2013). The field test indicated that the model can simulate the color-
mixing process in the aspects of the color-mixing order, and shape and position of the color-mixing spot. However,
the color-mixing spot was found not perfect. Xue et al (2016a) extracted primary colors from the background using a
k-means clustering algorithm to generate the color constraints. In addition, a spot template distribution algorithm was
proposed to generate camouflage patterns. Even this study achieved good results in terms of optical camouflage,
however, proposing this method to enhance an existing military camouflage would be difficult since it would change
totally the currently existing design.
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Proceedings of the International Conference on Industrial Engineering and Operations Management
Dubai, UAE, March 10-12, 2020
© IEOM Society International
A computational approach using an image quality assessment algorithm is therefore helpful in overcoming the
limitations of assessing camouflage when using human observers (Le et al., 2019; Goudarzi et al., 2012; Volonakis et
al., 2018; Yang et al., 2019). Previously, we developed Camouflage Similarity Index (CSI) to access the effectiveness
of selected military camouflage (Lin et al., 2014b). This image quality assessment algorithm was found outperformed
commonly used image quality algorithms such as UIQI (Lin et al., 2014b), MSE, PSNR, and SSIM (Lin et al., 2014a;
Patil & Pawar, 2017; More & Borse, 2017).
The purpose of this study was to apply a color similarity index for evaluating existing military camouflages designs.
The results of this study could be used for the improvement of the selected existing military camouflages. This study
contributed to the advancement of color similarity index to the existing military camouflage and the results would be
very useful for military research organizations, ministry of defense, and textile engineer.
2. Methodology
2.1 Existing Military Camouflages Collection
10 different existing camouflages from 10 different countries were selected in the current study. The camouflages were
obtained using Google search engine by typing “(name of country) camouflage” or “(name of country) military
uniform”. The image later was cut to 20x50 pixels using Adobe Photoshop CS6 for CSI calculation (Figure 1). Matlab
2018 was used to calculate overall L*, a*, and b* values of each camouflage (Table 1).
Figure 1. Adobe Photoshop CS6 was used to cut the image to 20x50 pixel (Lin et al., 2019b; Lin et al., 2019c)
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Proceedings of the International Conference on Industrial Engineering and Operations Management
Dubai, UAE, March 10-12, 2020
© IEOM Society International
Table 1. Overall L*, a*, and b* values of each camouflage
1
2
3
4
5
6
7
8
9
10
L* 75.3027
59.8683 58.0483 71.0352
55.7600 61.7693 70.3756 73.2909 55.0541 69.1774
a* -1.3365
-4.3183 1.6605 -1.0440
-4.3076 -10.3360 -2.0521 -18.6728 -2.4078 -0.1778
b* 9.4917 6.8219 17.2644 16.9246 -1.4285 10.3662 2.9419 13.9534 10.1500 13.3228
2.2 Background Collection
Considering different terrains and different climatic conditions, camouflaging is a challenging task for defense
applications (Karpagam et al., 2016; Brunye et al., 2018). At the moment, one camouflage is impossible to be applied
in all different terrains (Sparks, 2012). In the current study, one woodland background was selected. Canon EOS 5D
Mark II was used to capture the woodland background at 09:00 am (Figure 2).
Figure 2. Selected woodland background (Lin et al., 2019b; Lin et al., 2019c; Prasetyo, 2019)
14 different locations (backgrounds) from the woodland background were selected in the current study. 14 different
locations were selected to evaluate the camouflage effectiveness as a demonstration of evaluation in different
woodland environments since each location had different L*, a*, and b* values. Similar to camouflage, the image
location was cut to 20x50 pixels using Adobe Photoshop CS6 for CSI calculation. Matlab 2018 was also used to
calculate overall L*, a*, and b* values of each camouflage (Table 2).
Table 2. Overall L*, a*, and b* values of each background
1 2 3 4 5 6 7 8 9 10 11 12 13 14
L* 70.9752 76.1949
41.7265 47.4693 59.7803 59.3054 64.1585 52.4517 71.0048 67.8591 56.0665 51.3341 62.1479 78.3972
a* -4.7106 -4.1246
-4.0503 -0.8253 -7.8139 -7.8107 -8.5197 -7.8344 -5.9997 -7.9443 -6.3388 -6.3474 -4.1854 -7.2132
b* 12.4265 10.6737 2.2426 -1.1642 14.5979 14.5174 15.4299 13.2100 14.0740 15.7666 12.6836 9.3048 11.0375 16.9961
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Proceedings of the International Conference on Industrial Engineering and Operations Management
Dubai, UAE, March 10-12, 2020
© IEOM Society International
2.3 Color Similarity Index
Figure 3. CSI calculation chart (Lin et al., 2014b; Lin et al., 2019b; Lin et al., 2019c; Prasetyo, 2019)
2.4 Statistical Analysis
Minitab 18 was used to calculate the significant difference between the CSI value. Post-hoc analysis among ten
selected military camouflage was performed. The results were considered statistically significant when p ≤ 0.05 (Lin
et al., 2018; Lin et al., 2019a; Martinez et al., 2019; Miraja et al., 2019; Prasetyo et al., 2014; Prasetyo et al., 2019;
Torres et al., 2019).
RGB information collection
in matlab
RGB information collection
in matlab
Conversion to XYZ space in
matlab
Conversion to XYZ space in
matlab
Conversion to CIELAB
space in matlab
Conversion to CIELAB
space in matlab
Calculate the CSI between
camouflage and selected
background in matlab
Repeat the measurement in
other backgrounds
Cut the image to 20 x 50
pixels in Adobe Photoshop
CS6
Cut the selected location
to 20 x 50 pixels in Adobe
Photoshop CS6
Find the camouflage using
Google search engine
Camouflage Background
Capture woodland
background using Canon
EOS 5D
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Proceedings of the International Conference on Industrial Engineering and Operations Management
Dubai, UAE, March 10-12, 2020
© IEOM Society International
3. Results and Discussion
The purpose of this study was to investigate the camouflage effectiveness across selected countries using Camouflage
Similarity Index (CSI). CSI ranges from 0 to 1 and the best value 0 is achieved if camouflage perfectly blends with
the background.
The CSI results are presented in Table 3. Based on Table 3, design 1 had the lowest CSI on background 1. Design 4
had the lowest CSI on background 1 and 14, design 5 had the lowest CSI on background 3 and 4, design 6 had the
lowest CSI on background 5,6,7,8,10,11,12,13, and design 10 had the lowest on background 9. Regarding the highest
CSI on each background, design 1 had the highest CSI on background 1, design 5 had the highest CSI on background
1,2,5,6,7,8,9,10,11,12,13, and design 8 had the highest CSI on background 3,4,12,13.
Table 3. Camouflage Similarity Index (CSI) results Background Camouflage Similarity Index (CSI)
1
2
3
4
5
6
7
8
9
10
Background 1 0.6883 0.8428 0.8275 0.6768** 0.9639* 0.7044 0.8623 0.8687 0.7975 0.7226
Background 2 0.6725** 0.8932 0.8855 0.7906 0.9605* 0.8384 0.7761 0.9009 0.8668 0.8026
Background 3 0.9842 0.9052 0.9464 0.9762 0.7823** 0.9406 0.9434 0.9931* 0.9155 0.9699
Background 4 0.9823 0.8615 0.9581 0.9826 0.6523** 0.9638 0.9237 0.9962* 0.9483 0.9732
Background 5 0.8857 0.8663 0.9085 0.8288 0.9643* 0.7638** 0.9157 0.8908 0.8664 0.8713
Background 6 0.8928 0.8637 0.9095 0.8331 0.9631* 0.7605** 0.9099 0.8911 0.8670 0.8781
Background 7 0.8784 0.8765 0.8922 0.7930 0.9517* 0.6964** 0.9101 0.8513 0.8118 0.8693
Background 8 0.9468* 0.8954 0.9210 0.9244 0.9393 0.7977** 0.9446 0.9449 0.8647 0.9039
Background 9 0.7892 0.9049 0.8943 0.8100 0.9554* 0.8238 0.8924 0.8945 0.8449 0.7715**
Background 10 0.8300 0.8341 0.8855 0.7425 0.9614* 0.6854** 0.9058 0.8519 0.8090 0.7989
Background 11 0.9120 0.9162 0.9305 0.8804 0.9472* 0.8448** 0.9457 0.9129 0.8932 0.8854
Background 12 0.9368 0.8554 0.9008 0.9107 0.8322 0.7330** 0.9025 0.9586* 0.8531 0.9135
Background 13 0.8031 0.8342 0.8557 0.7817 0.9025 0.7686** 0.8856 0.9068* 0.8514 0.8287
Background 14 0.7603 0.9037 0.8834 0.7250** 0.9900* 0.8859 0.9123 0.7904 0.8876 0.8320
Average 0.8545 0.8752 0.8999 0.8326 0.9119 0.8005 0.9022 0.9037 0.8627 0.8586
Max 0.9842 0.9162 0.9581 0.9826 0.9900 0.9638 0.9457 0.9962 0.9483 0.9732
Min 0.6725 0.8341 0.8275 0.6768 0.6523 0.6854 0.7761 0.7904 0.7975 0.7226
Regarding the overall performance, design “6” was found had the lowest average CSI (0.8005) and design “5” had
the highest average CSI (0.9119) (Figure 4). However, even design “6” had the lowest CSI, this design was found not
suitable to be applied in dark woodland environment such as backgrounds 3 and 4. In these backgrounds, design “5”
was found had the lowest CSI with the value of 0.7823 and 0.6523 respectively.
One-way ANOVA was applied to test the significance of the design to CSI. Based on Table 4, it was that there was a
significant effect of design to the CSI. Tukey HSD test was applied to test the significance of multiple comparisons.
This test can simultaneously run the set of all pairwise comparisons and identifies any difference between two means
that is greater than the expected standard error (Table 5). Interestingly, there was 3 different groups which consist of
group A (design 6), group B (4,1,10,9,2), and group C (3,7,8,5).
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Proceedings of the International Conference on Industrial Engineering and Operations Management
Dubai, UAE, March 10-12, 2020
© IEOM Society International
Figure 4. Interval Plot of CSI vs Design.
Table 4. ANOVA for Design vs CSI.
Source DF Adj SS Adj MS F-Value P-Value
Design 9 0.1610 0.017891 3.66 0.000
Error 130 0.6357 0.004890
Total 139 0.7967
Table 5. Means, SDs, and Tukey HSD test result of “S” country
Design Mean StDev Group
6 0.8005 0.0874 A
4 0.8326 0.0921 AB
1 0.8545 0.1007 AB
10 0.8586 0.0714 AB
9 0.8627 0.0411 AB
2 0.8752 0.0280 AB
3 0.8999 0.0341 B
7 0.9022 0.0431 B
8 0.9037 0.0563 B
5 0.9119 0.0941 B
Despite the substantial and clear study results, the authors would like to acknowledge the limitations of the current
study. First, the lack of proper military camouflage collection. Instead of using the proper design, we obtained the
camouflages by using the Google search engine. This would lead to unbalance color distribution. Moreover, the
sunlight or when the picture was taken would definitely affect the results. Second, the CSI values were limited to the
selected woodland background which was taken at 09:00 am. Different sunlight, terrains, and thermal conditions
would influence the environment which subsequently influences the CSI values. Future research to assess the selected
camouflage in dessert or other terrains would be a promising research topic.
4. Conclusions
Military camouflage is an important part of defense technology. It is designed to confuse the enemy by visually
merging the outline of military design to the surrounding environment. The purpose of this study was to apply a color
similarity index for evaluating existing military camouflages designs. Camouflage Similarity Index (CSI) was utilized
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Proceedings of the International Conference on Industrial Engineering and Operations Management
Dubai, UAE, March 10-12, 2020
© IEOM Society International
as a color similarity index and the value varies between 0 to 1. The best value of 0 is achieved if the selected existing
military camouflage design perfectly blends with the surrounding environment. 10 existing military camouflage
designs from different regions were evaluated under 14 different locations in the swamp environment. The results
indicated that the CSI was an effective tool for identifying the effectiveness of existing military camouflage designs
across regions. Interestingly, even the CSI values were found different among 10 selected designs, Post-hoc Tukey
HSD test revealed that there was statistical difference between each design and it could be categorized into 3 different groups. This study contributed to the advancement of color similarity index to the existing military camouflage and
the results would be very useful for military research organizations, ministry of defense, and textile engineer.
Acknowledgments
The authors would like to thank Nio Dolly Siswanto for his invaluable supports in this study.
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Biography
Yogi Tri Prasetyo is currently an associate professor in the School of Industrial Engineering and Engineering
Management, Mapúa University, Philippines. He received a Bachelor of Engineering in Industrial Engineering from
Universitas Indonesia (2013). He also studied for one year (2011-2012) at Waseda University, Japan, during his junior
year as an undergraduate exchange student. He received an MBA (2015) and a Ph.D. (2019) from Department of
Industrial Management National Taiwan University of Science and Technology (NTUST), with a concentration in human factors and ergonomics. He was awarded as NTUST Outstanding Youth with a perfect GPA 4.00. He has a
wide range of research interests including human-computer interaction particularly related to eye movement, color
optimization of military camouflage, strategic product design, usability analysis, and now he is currently doing
accident analysis and prevention. He published several SCI journals in Displays, Color Research and Application,
Journal of Eye Movement Research, several non-SCI journals, and several conference proceedings. In addition,
Dr.Yogi has contributed to several international conferences as co-chair, chair session, and even committee members.
Apart from academics, Dr.Yogi likes playing flute, judo, swimming, and hiking. He has two black belts (1st dan black
belt judo and 1st dan black belt taekwondo), an international certified lifeguard, and a certified advanced diver. He
speaks Indonesian, English, Chinese, Japanese, and currently, he’s working hard for his Filipino.
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