Color Matching of Pre-colored Fiber Blends Based on Two-Constant Kubelka-Munk Theory

Some modifications of two-constant Kubelka-Munk theory has been made for the color matching of pre-colored fiber blends. The scattering coefficients of three primaries are averaged and taken as initial coefficients in each wavelength. The unit sums of concentration in E-Allen’s Algorithm were removed in the case of pre-colored fiber blends. Predicted blend ratios and reflectance curves agree very well with measured ones and color differences are within the tolerant limit, showing satisfactory matching results.

The Three-region Method of Color Matching in the Presence of Fluorescent Whitening Agents

An expression of reflectance of fluorescent dyes in emission region was used in this article. Through the analysis of the spectral radiance factor of fluorescent whitening agents(FWA), the color matching of FWA was studied and the three-region method of color matching method was put forward. The method was proved to be reliable for the dyes used and was easy to apply for the color matching of FWAs.

Color Matching for Fiber Blends Based on Stearns-Noechel Model

Prediction of the formula for matching a given color standard by blending pro-dyed fibers is of considerable importance to the textile industry. This kind of formulation suffers from a lack of computer-aided tool to assist the colorist attempting to find a good recipe to reproduce a target color. In this article a tristimulus color matching algorithm based on Stcarns-Noechel model is proposed. This algorithm was run to predict recipes for 36 viscose blends. The maximum color difference is 0.97 CIELAB units. It is demonstrated that the algorithm can be used in color matching of fiber blends.

A Four-color Matching Method Combining Neural Networks with Genetic Algorithm

A brief review of color matching technology and its application of printing RGB images by CMY or CMYK ink jet printers is presented, followed by an explanation to the conventional approaches that are commonly used in color matching. Then, a four color matching method combining neural network with genetic algorithm is proposed. The initial weights and thresholds of the BP neural network for RGB to CMY color conversion are optimized by the new genetic algorithm based on evolutionarily stable strategy. The fourth component K is generated by using GCR (Gray Component Replacement) concept. Simulation experiments show that it is well behaved in both accuracy and generalization performance.

Smart Clothing Fabric Color Matching with Reference to Popular Colors

Color economy and market fashion trend have an increasing impact on clothing fabric color matching.Therefore,a smart clothing fabric color matching system with reference to popular colors is designed to realize the diversification of clothing color matching,which includes a palette generation module and a clothing fabrics-palette color matching network(CF-PCN).Firstly,palette generation module generates palettes referring popular colors while maintains color styles of clothing fabrics.Secondly,CF-PCN generates color matching images containing color information of palettes.The experimental results show that the color matching system has a higher average pixel ratio of palette colors and contains more palette color information.It demonstrates that the system achieves color matching innovation referring popular colors while retaining color style of clothing brands and provides designers with appropriate color matching solutions.

Quantum Efficiency of Fluorescent Dyes and Color Matching

Because of the special optical characters, the color matching of fluorescent dyes is quite complicated. In order to find the algorithm of the color matching of fluorescent dyes, some experiments and measurements of one kind of fluorescent dye were carried out. An elementary probe into the method of color matching of fluorescent dyes has been made through the expression deduced by James S. Bonham and standard Kubelka-Munk theory. The results prove that the method has a great applicability for the color matching of fabric dyed with only one kind of fluorescent dye.

Application of nonlinear color matching model to four-color ink-jet printing

Through discussing the color matching technology and its application in printing industry the conventional approaches commonly used in color matching, and the difficulties in color matching, a nonlinear color matching model based on two step learning is established by finding a linear model by learning pure color data first and then a nonlinear modification model by learning mixed color data. Nonlinear multiple regression is used to fit the parameters of the modification model. Nonlinear modification function is discovered by BACON system by learning mixture data. Experiment results indicate that nonlinear color conversion by two step learning can further improve the accuracy when it is used for straightforward conversion from RGB to CMYK. An improved separation model based on GCR concept is proposed to solve the problem of gray balance and it can be used for three to four color conversion as well. The method proposed has better learning ability and faster printing speed than other historical approaches when it is applied to four color ink jet printing.

Image matching algorithm based on SIFT using color and exposure information

Image matching based on scale invariant feature transform（SIFT） is one of the most popular image matching algorithms, which exhibits high robustness and accuracy. Grayscale images rather than color images are generally used to get SIFT descriptors in order to reduce the complexity. The regions which have a similar grayscale level but different hues tend to produce wrong matching results in this case. Therefore, the loss of color information may result in decreasing of matching ratio. An image matching algorithm based on SIFT is proposed, which adds a color offset and an exposure offset when converting color images to grayscale images in order to enhance the matching ratio. Experimental results show that the proposed algorithm can effectively differentiate the regions with different colors but the similar grayscale level, and increase the matching ratio of image matching based on SIFT. Furthermore, it does not introduce much complexity than the traditional SIFT.

基于深度表征学习和遗传算法的军用座舱色彩设计方法

军用座舱色彩设计作为载人军事设备工业设计中较为主观的一部分,设计的合理性至关重要。为提高军用座舱色彩设计的科学性,提出一种基于深度表征学习和遗传算法的军用座舱配色方法。利用深度表征学习模型预测军用座舱配色方案,并根据色彩感知理论建立军用座舱配色模型,将其作为生成方案的限制条件。同时,引入交互式遗传算法到智能配色系统中,通过人工引导的方式优化神经网络的参数,对预测的配色方案进行有效迭代。实验结果表明:该方法生成的配色方案符合军用座舱配色模型,结合遗传算法的模型预测准确率比单一的深度表征模型提高了16%~18%。相较于人工色彩设计方案,军用座舱智能配色方法生成的方案满意度略优、设计周期缩短了80%~88%,色彩稳定性提高了6%~12%。

数学对服装色彩的匹配与调和的影响

在服装设计中,色彩的选择主要依靠设计师的直觉与经验,带有一定的主观性与不确定性,但在科学技术的发展以及多学科交叉领域的不断发展下,运用数学可以在服装设计中进行合理的颜色匹配与调和,可以有效提升产品的品质。因此,针对数学对服装色彩的匹配与调和的影响展开探究,以期为服装设计师提供理论参考。

数字化配色技术在色纺纱领域的应用

数字化配色技术有利于纺织行业的数字化和智能化体系建设和发展,能够更好地满足市场和消费者需求。文章分析了目前数字化配色技术的最新发展和应用领域,提出了数字化配色技术的概念以及应用于色纺纱生产中的具体步骤,阐述了数字化配色技术应用在色纺纱领域的优点和局限性,并通过具体案例指明了数字化配色技术在色纺企业实际开发应用中的重要作用。

基于改进随机森林回归模型的木材染色配方预测研究

为提高木材染色计算机智能配色的准确性和实用性,选择(樟子松)单板为染色材,以随机森林回归模型为基础,先用随机搜索算法(RS)寻找模型最优参数,再引入主成分分析法(PCA)进行特征提取,并以配方的均方误差作为评价指标。与固定参数的随机森林回归模型(RFR)模型及其他模型相比,优化后的模型(PCA-RS-RFR)配方均方误差为0.00491,相较于固定参数RFR(0.00695)、RS-RFR(0.00618)等具有明显优势,运行效率也较出色。

京族文创服饰设计中海洋元素的运用策略研究

新时期,随着社会公众消费水平的不断提升,在满足基本温饱需求后,人们更加注重精神需求、文化需求的满足。文创产品在此背景下得到快速发展,且深受民众的欢迎。将民族文化融入文创作品中,不仅能促进文创产品的创新,还能让产品具有特别的文化寓意,让文创产品讲好文化故事,得到大众的认可。文章研究了京族文创服饰设计中海洋元素的运用策略,旨在为我国文创设计产业提供创新思路与借鉴,在推动文创产业发展的同时,推动少数民族文化的传承与弘扬。

新媒体背景下视觉传达设计中色彩发展趋势

探索新媒体时代视觉传达设计中色彩的发展趋势,提高设计中色彩的合理性。分析色彩在视觉传达领域的重要作用,引出新媒体大背景环境下色彩应用在视觉传达设计中的地位,并以动态海报设计、UI设计、标志设计的实际出发,分析色彩在其中的变化方法及原理。得到色彩在新媒体时代视觉传达设计中的发展趋势。无论是在什么时期,色彩在视觉传达设计中都是必不可少的元素,通过时代的变化,色彩应用于新媒体技术上,色彩表现方法与内容也更加丰富,但这更需要设计师时刻警醒自己运用好色彩,提升视觉传达设计在新媒体领域的潜力。

Cross-CNN:基于CNN和Transformer混合模型的动画跨帧线稿着色算法

对长序列的动画线稿帧进行着色是计算机视觉中一项具有挑战性的任务.一方面,线稿中包含的信息较为稀疏,需要着色算法对缺失的信息进行推断;另一方面,连续帧之间的色彩需要保持一致,以确保整个视频的视觉质量.现有的着色算法多数只针对单张图片进行着色,这类算法只给出一个开放性的符合合理范围的色彩结果,无法适用于帧序列着色.另一些基于参考帧的着色算法,并没有将2帧之间的关系有机地联系起来,导致着色效果不够出色.在同一镜头序列中,同一对象的特征往往不会发生太大变化,因此,可以设计一个根据给定参考帧,即可给线稿自动着色的模型.为此,本文提出了基于CNN(Convolutional Neural Networks)和Transformer相结合的模型Cross-CNN,该模型能够从参考帧中寻找并匹配颜色,从而保证时间维度上的特征一致性.Cross-CNN模型参考帧和线稿帧在通道维度叠加,输入预训练的Resnet50网络提取局部融合特征,将融合特征图传给Transformer结构进行编码以提取全局特征.在Transformer结构中设计了交叉注意力机制更好地匹配远距离特征.最后使用带有跳层连接的卷积解码器完成着色图片输出.本文在数据集方面从8部电影中截取画面并经过严格筛选,最终制作了一个包含20000对二元组的数据集用于实验研究.Cross-CNN的SSIM(Structural SIMilarity)达到了0.932,高于SOTA算法0.014.本文算法代码链接:https://github.com/silenye/Cross-CNN.

浅谈地域文化在动画设计中的应用与实践

我国地域文化体系本身具有较强的亲和力和感染力,其在我国动画设计领域中也起到不可替代的作用。设计者应正确把握地域文化与动画设计之间的关联,从造型艺术、民间曲艺、民间工艺、民间故事、色彩搭配等角度着手深化地域文化在动画设计中的应用,重点分析地域文化在动画设计中的实践,为我国动画产业发展注入鲜活的地域文化元素,进一步推动我国创新文化产业的可持续发展。

面向海岛海岸带区域的高分遥感影像智能化色彩增强方法

原始高空间分辨率海岛海岸带遥感影像往往存在影像灰暗、偏色、地物信息较难辨识的现象。为及时获取清晰、信息丰富、反差适中、亮度均匀的海岛礁遥感影像,满足日益强烈的海岛海岸带地理信息保障需求,针对海岛海岸带高空间分辨率遥感影像,该文提出一种深度学习结合改进直方图匹配的智能化调色方法。首先,进行数据重采样与自适应分块获取抽稀影像;其次,应用MBLLEN网络对抽稀影像进行真彩色增强;最后,采用改进直方图匹配的方法对原始影像进行色彩映射,最终得到符合人眼视觉、色彩一致、细节丰富的遥感影像。采用主客观相结合的方式综合评价调色效果,结果表明:相较于Retinex,HE和MASK等常用调色方法,该文算法结果更符合人眼视觉、色彩一致、细节丰富,可有效改善海岛海岸带高空间分辨率遥感影像视觉效果,较好地保留原始地物的细节信息,大幅提升调色效率。

颜料与线密度对原液着色涤纶短纤维及纱线颜色的影响

为掌握原液着色涤纶短纤维的颜色对其纱线颜色变化的影响规律,选取红、黄、蓝3种颜色半消光原液着色涤纶短纤维,根据实际生产中主要调整的参数,系统研究了颜料质量分数、纤维线密度对其色度值的影响规律;同时,考察了从纤维到纱线(线密度为20 tex,捻系数为300)的色度值变化,并与特定规格半消光染色短纤维、大有光原液着色短纤维进行对比。结果表明:颜料质量分数显著影响半消光纤维的色度值,在浅、中色之间的差异尤为明显,是中、深色之间的2倍;线密度对纤维色差(ΔE_(CMC(2∶1)))的影响不大,黄色的色差变化较红色、蓝色更明显;半消光原液着色短纤维到纱线的色度值变化不明显,色差在0.8~2.6范围内;在半消光染色纤维中,黄色的色差变化偏大,为3.4~3.9,红色、蓝色与原液着色的色差相当;大有光原液着色涤纶短纤维到纱线的变化与半消光染色短纤维的更为相似,二者的色差ΔE_(CMC(2∶1))、明度差ΔL、饱和度差ΔC以及色调差ΔH基本相同,说明对于特定的颜色类型,可借鉴染色纱线的配色经验,为原液着色纱线配色模型的系数调整提供支持。

基于吸收光谱的计算机配色方法及其在分散染料拼混中的应用

依据朗伯-比尔定律和吸光度的加和原理,在可见光范围(380~780 nm)内基于超定方程组算法开发了一款用于拼混染料配色的计算机软件。阐述了软件用于拼混配色时,各染料组分最佳浓度的计算原理和方法。提出从颜色和光谱两方面筛选配方中成分以减少无效配方,然后利用该软件对3只分散染料标样(分散橙30、分散蓝183∶1和分散黑ECT)分别实施光谱颜色复制,获得与标样具有相同颜色且光谱特性也一致的不同拼混染料配方。将这些配方按其拼混比制成新的商品染料,用于不同颜色深浅的处方和不同面料的染色,与标样所在对照组进行染色结果对比,发现拼混染料同样实现了光谱颜色复制,且颜色深浅和面料差异未对结果造成影响。

苏木与红花或栀子色素对棉的拼色研究

采用天然色素对改性棉织物进行媒染拼色染色,探讨苏木与红花或栀子色素的拼色染色性能,研究媒染剂对拼色染色性能的影响,测试拼色染色织物的色牢度。结果表明,苏木与红花或栀子色素拼色可使棉织物获得丰富的色泽,媒染剂对拼色染色织物的色相有较大影响,随染料浓度变化染色织物的颜色特征值呈规律变化;媒染拼色染色织物的耐水洗、摩擦和日晒色牢度均在3级及以上。