MF-CFI: A fused evaluation index for camouflage patterns based on human visual perception

The evaluation index of camouflage patterns is important in the field of military application. It is the goal that researchers have always pursued to make the computable evaluation indicators more in line with the human visual mechanism. In order to make the evaluation method more computationally intelligent, a Multi-Feature Camouflage Fused Index (MF-CFI) is proposed based on the comparison of grayscale, color and texture features between the target and the background. In order to verify the effectiveness of the proposed index, eye movement experiments are conducted to compare the proposed index with existing indexes including Universal Image Quality Index (UIQI), Camouflage Similarity Index (CSI) and Structural Similarity (SSIM). Twenty-four different simulated targets are designed in a grassland background, 28 observers participate in the experiment and record the eye movement data during the observation process. The results show that the highest Pearson correlation coefficient is observed between MF-CFI and the eye movement data, both in the designed digital camouflage patterns and large-spot camouflage patterns. Since MF-CFI is more in line with the detection law of camouflage targets in human visual perception, the proposed index can be used for the comparison and parameter optimization of camouflage design algorithms.     

A platform for initial testing of multiple camouflage patterns

The human visual system is still an important factor in military warfare; military personnel receive training on effective search strategies, and camouflage that can effectively conceal objects and personnel is a key component of a successful integrated survivability strategy. Previous methods of camouflage assessment have, amongst others, used psychophysics to generate distinctiveness metrics. However, the population from which the human observers are drawn is often not well defined, or necessarily appropriate. In this experiment we designed a new platform for testing multiple patterns based on a camouflaged object detection task, and investigate whether trained military observers perform better than civilians. We use a two-alternative forced choice paradigm, with participants searching images of woodland for a replica military helmet displaying Olive Green, Multi Terrain Pattern, US Marine Pattern or, as a conspicuous control, UN Peacekeeper Blue. Our data show that there is no difference in detection performance between the two observer groups but that there are clear differences in the effectiveness of the different helmet colour patterns in a temperate woodland environment. We conclude that when tasks involve very short stimulus presentation times, task-specific training has little effect on the success of target detection and thus this paradigm is particularly suitable for robust estimates of camouflage efficacy.     

Improvement of VIS and IR camouflage properties by impregnating cotton fabric with PVB/IF-WS2

In order to examine the possibility to improve its camouflage properties standard cotton fabric with camouflage print was impregnated with poly(vinyl butyral), PVB and fullerene-like nanoparticles of tungsten disulfide, PVB/IF-WS₂. FTIR analysis excluded any possible chemical interaction of IF-WS₂ with PVB and the fabric. The camouflage behavior of the impregnated fabric has been examined firstly in the VIS part of the spectrum. Diffuse reflection, specular gloss and color coordinates were measured for three different shades (black, brown and dark green). Thermal imaging was applied to examine the camouflage abilities of this impregnation in IR part of the spectrum. The obtained results show that PVB/IF-WS₂ impregnation system induced enhacement of the materials camouflage properties, i.e. that IF-WS₂ have a positive effect on spectrophotometric characteristics of the fabric.     

地空导弹阵地伪装及效果评价

分析了某型地空导弹武器阵地的暴露特征,根据伪装学的原理,针对这些特征采取了相应的伪装措施,并根据观测试验数据,对该型地空导弹武器阵地伪装前后的光学、热红外及雷达伪装效果进行了定量评价分析。结果表明,在伪装前完全暴露的阵地,伪装后目标的暴露特征大大减小,伪装效果非常明显。     

Evaluation of infrared camouflage effectiveness via a multi-fractal method

This paper reports an alternative approach to the evaluation of infrared camouflage effectiveness via a multi-fractal method. By calculating multi-fractal spectra of the target region and the background re-gions in an infrared image, the spectrum shape features and the discrete Fréchet distances among these spectra were used to analyze the camouflage effectiveness of the target qualitatively and quantitatively, and the correlation coefficients of the spectra were further used as the index of camouflage effectiveness. It was found that the camouflaged target had better camouflage effectiveness than the target without camouflage in the same one background, and the same one camouflaged target had different camouflage effectiveness in different backgrounds. On the whole, the target matching well with its background had high camouflage effectiveness value. This approach can expand the application of multi-fractal theory in infrared camouflage technology, which should be useful for the research of infrared camouflage mate-rials, the design of camouflage patterns as well as the deployment of military equipment in battlefield.     

一种雷达波衰减型红外迷彩伪装遮障材料设计方案

伪装遮障以其伪装性能好、适应能力强、作业简单、携带方便而在现代战争中被广泛采用。在对遮障材料性能要求分析的基础上,提出了一种新型雷达波衰减型红外迷彩伪装遮障材料的设计方案。其在可见光、近红外、雷达波段均具有良好的反探测性能,可有效对抗现代高技术侦察手段和设施。     

伪装和机动对野战指挥所生存能力影响定量研究

对在敌航空兵编队采用普通炸弹轰炸和精确制导武器攻击两种模式下,研究了我野战指挥所被毁伤的概率模型,并探讨了我野战指挥所采取伪装和机动措施后被毁伤概率的模型,最后利用模型分析计算了在采取伪装和机动措施情况下,我野战指挥所被毁伤的概率的变化规律,得到了比较合理、可靠的数据,验证了该模型的合理性,也为指挥员合理规划伪装和适时机动提供了数量依据。     

伪装指数与生存能力的关系模型及求解

建立了战场伪装与系统生存能力的定量关系模型, 用其可以解析地计算系统被发现概率; 同时, 根据生存能力指标可以实现伪装指数的分解与合成, 为战场建设的总体规划提供依据     

Background dominant colors extraction method based on color image quick fuzzy c-means clustering algorithm

A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design. This paper proposes a Color Image Quick Fuzzy C-Means (CIQFCM) clustering algorithm based on clustering spatial mapping. First, the clustering sample space was mapped from the image pixels to the quantized color space, and several methods were adopted to compress the amount of clustering samples. Then, an improved pedigree clustering algorithm was applied to obtain the initial class centers. Finally, CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image. After theoretical analysis of the effect and efficiency of the CIQFCM algorithm, several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm. The results indicated that the value of quantization intervals should be set to 4, and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect. In addition, as the image size increased from 128 × 128 to 1024 × 1024, the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times, which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.