Developing a similarity index for static camouflaged target detection

Lin, Chang-Hong; Chang, C-C; Yh, Lee

doi:10.1179/1743131x13y.0000000057

Cited by 13 publications

(32 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following our previous publications, the target was resized to simulate 75 m distance with the height of 170 cm . The distance between participant and the monitor was 55 cm, making the camouflaged human‐shaped target height 45.3 pixels on the display screen (1920 × 1200 pixels) …”

Section: Methodsmentioning

confidence: 65%

“…Our previous research proposed camouflage similarity index (CSI) to access the performance of military camouflage . The proposed index is based on human color perception.…”

Section: Introductionmentioning

confidence: 72%

“…The third stage is the evaluation of similarity index using the existed index. UIQI, MSE, PSNR, and SSIM are some indexes which can be applied to analyze the similarity index without using human subject. These indexes can be used as preliminary assessment before using human‐subject test.…”

Section: Discussionmentioning

confidence: 99%

“…CSI measured the color difference between camouflage and background pixel to pixel applying CIE 1976 L * a * b * color system and NBC color difference as the basis calculation . It ranges from 0 to 1 and the best value 0 is achieved if camouflage perfectly blends with the background . Thus, minimizing CSI value can enhance the concealment of the outline soldiers.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, minimizing CSI value can enhance the concealment of the outline soldiers. Our index also outperformed commonly used image indexes such as UIQI, MSE, PSNR, and SSIM …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimization of color design for military camouflage in CIELAB color space

Lin

Prasetyo

Siswanto

et al. 2019

Color Research & Application

View full text Add to dashboard Cite

The assessment of military camouflage is a key consideration in the modern military field. Traditionally, the assessment relies on traditional human visual detection tests because a large scale multi‐level and multi‐factor experiments are time‐ and resource‐consuming. One aspect of camouflage assessment, to which this current study pertains, entails improving upon or “enhancing” an existing or “selected” design. The current study presents a new and practical approach for enhancing the selected military camouflage by utilizing response surface methodology (RSM) of %L*, %a*, and %b* in CIELAB color space. Ten participants were recruited to evaluate 35 variations of %L*, %a*, and %b* on camouflage similarity index (CSI) and reaction time (RT). Based on RSM, the optimum combination occurs at L*: 61.4966, a*: −5.6505, and b*: 10.5114. In addition, a predictive algorithm to calculate the optimum shift of %L*, %a*, and %b* from the original camouflage to the improved camouflage derived from RSM is also proposed. The optimum shift occurs at −25%L*, −55%a*, and + 80%b*. In the end, a new design guideline is proposed for the enhancement of selected military camouflage, which adopts the present study's research findings.

show abstract

Section: Methodsmentioning

confidence: 65%

“…Our previous research proposed camouflage similarity index (CSI) to access the performance of military camouflage . The proposed index is based on human color perception.…”

Section: Introductionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Thus, minimizing CSI value can enhance the concealment of the outline soldiers. Our index also outperformed commonly used image indexes such as UIQI, MSE, PSNR, and SSIM …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimization of color design for military camouflage in CIELAB color space

Lin

Prasetyo

Siswanto

et al. 2019

Color Research & Application

View full text Add to dashboard Cite

show abstract

A metaheuristic‐based approach to optimizing color design for military camouflage using particle swarm optimization

Lin

Prasetyo

2019

Color Research & Application

View full text Add to dashboard Cite

Enhancing an existing military camouflage is an important component during the assessment of military camouflage. The current study proposed a new and practical approach to enhancing the undetectability of a military camouflage using particle swarm optimization (PSO). Eight different locations (20 × 50 pixels) in the one swamp background were selected to be the place of a human-shaped target. The PSO would generate newly proposed camouflage as an empirical parameter based on the lower and the upper bounds from selected four different colors in swamp background. The predictive algorithm was applied to adjust the optimum shift of %L*, %a*, and %b* from the original to the empirical parameter. Thirty participants were recruited to evaluate the original and newly proposed camouflages. Paired sample t test indicates that the newly proposed military camouflage had a significant lower camouflage similarity index value and a longer detection time. The PSO shows to be a method with good results; however, a comprehensive study using multiple backgrounds and patterns would be required to generalize the methodology to other background environments or camouflage patterns. K E Y W O R D Scamouflage similarity index, detection time, military camouflage, particle swarm optimization, target search

show abstract

Research status and development trend of image camouflage effect evaluation

Jiao

et al. 2022

Multimed Tools Appl

View full text Add to dashboard Cite

Developing a similarity index for static camouflaged target detection

Cited by 13 publications

References 18 publications

Optimization of color design for military camouflage in CIELAB color space

Optimization of color design for military camouflage in CIELAB color space

A metaheuristic‐based approach to optimizing color design for military camouflage using particle swarm optimization

Research status and development trend of image camouflage effect evaluation

Contact Info

Product

Resources

About