Space Color Design of Medical and Nursing Institutions Based on Adaptive Aging Characteristics

With the acceleration of China’s aging process and the rapid development of social economy,the government’s strong support for the integration of medical and nursing has made the construction of medical and nursing buildings more efficient.At the same time,the need for old-age care is more diversified and hierarchical,and the life cycle is more obvious.Designing an aging color environment for the elderly with different visual abilities in medical care buildings has become a problem for investors,operators and designers.This paper takes the indoor color of medical and nursing buildings as the research object,and combines the characteristics of the elderly’s visual acuity and the ratio and efficacy of indoor colors to investigate two high-end medical and nursing institutions in Beijing and Nanjing.Based on the physical,psychological and behavioral needs,this paper explores the appropriate aging color design method for the living space of the elderly in the medical institutions,aiming to make a meagre effort for the in-depth research and practice of indoor environment design of medical institutions in the future.

IAACS: Image aesthetic assessment through color composition and space formation

Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics of the general public. Although deep learning methods have successfully learned good visual features from images,correctly assessing the aesthetic quality of an image remains a challenge for deep learning. Methods To address this, we propose a novel multiview convolutional neural network to assess image aesthetics assessment through color composition and space formation(IAACS). Specifically, from different views of an image––including its key color components and their contributions, the image space formation, and the image itself––our network extracts the corresponding features through our proposed feature extraction module(FET) and the Image Net weight-based classification model. Result By fusing the extracted features, our network produces an accurate prediction score distribution for image aesthetics. The experimental results show that we have achieved superior performance.

Strategies for Applying LED Lights in Night Space Lighting for Bridges

LED lights have been widely used in urban night space lighting in recent years as they are small,energy-saving,and efficient.This article explores the use of LEDs in bridge night space lighting and their application strategies.The aim is to offer valuable insights and references for urban planners and bridge lighting designers in China.By advancing the application of LED technology in bridge night lighting,the goal is to enhance the city’s nighttime ambiance,making the bridge an iconic landmark and a defining feature of the city.

Performance evaluation of wavelet scattering network in image texture classification in various color spaces

The optimized color space is searched by using the wavelet scattering network in the KTH_TIPS_COL color image database for image texture classification. The effect of choosing the color space on the classification accuracy is investigated by converting red green blue （RGB） color space to various other color spaces. The results show that the classification performance generally changes to a large degree when performing color texture classification in various color spaces, and the opponent RGB-based wavelet scattering network outperforms other color spaces-based wavelet scattering networks. Considering that color spaces can be changed into each other, therefore, when dealing with the problem of color texture classification, converting other color spaces to the opponent RGB color space is recommended before performing the wavelet scattering network.

Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.

Color Measurement of Segmented Printed Fabric Patterns in Lab Color Space from RGB Digital Images

Colors of textile materials are the first parameter of quality evaluated by consumers and a key component considered in selecting printed fabric. In the textiles industry, digital printed fabric analysis is one of the basic elements in successfully utilizing a color mechanism scheme and objectively evaluating fabric color alterations. Precise color measurement, however, is mostly used in sample analysis and quality inspection which help to produce reproducible or similar product. It is important that for quality inspection, the color of the product should be measured as a necessary requirement of quality control whether the product is to be accepted or not. Presented in this study is an unsupervised segmentation of printed fabrics patterns using mean shift algorithm and color measurements over the segmented regions of printed fabric patterns. The results established a consistent and reliable color measurement of multiple color patterns and appearance with the established range without any interactions.

Deer Body Adaptive Threshold Segmentation Algorithm Based on Color Space

In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer.Also,when the target and background grey values are similar,the multiple background targets cannot be completely separated.To better identify the posture and behaviour of deer in a deer shed,we used digital image processing to separate the deer from the background.To address the problems mentioned above,this paper proposes an adaptive threshold segmentation algorithm based on color space.First,the original image is pre-processed and optimized.On this basis,the data are enhanced and contrasted.Next,color space is used to extract the several backgrounds through various color channels,then the adaptive space segmentation of the extracted part of the color space is performed.Based on the segmentation effect of the traditional Otsu algorithm,we designed a comparative experiment that divided the four postures of turning,getting up,lying,and standing,and successfully separated multiple target deer from the background.Experimental results show that compared with K-means,Otsu and hue saturation value(HSV)+K-means,this method is better in performance and accuracy for adaptive segmentation of deer in artificial breeding scenes and can be used to separate artificially cultivated deer from their backgrounds.Both the subjective and objective aspects achieved good segmentation results.This article lays a foundation for the effective identification of abnormal behaviour in sika deer.

Wide-Band Multi-spectral Space for Color Representation

This paper develops a wide-band multi-spectral space for color representationwith Aitken PCA algorithm. This novel mathematical space using the broad-band spectra matchingmethod aims at improving the accuracy of color representation as well as reducing costs forprocessing and storing multi-spectral images. The results show that the space can present ourexperimental original spectral spaces (i. e. Munsell color matt and DIN-6164 color chips) with highefficiency, and that the spanning space with three eigenvectors can present the original space atmore than 98% CSCR, and when 5 eigenvectors are used it can cover almost the whole o-riginal spaces.

A Study on the Influence of Luminance L* in the L*a*b* Color Space during Color Segmentation

In this paper an evaluation of the influence of luminance L* at the L*a*b* color space during color segmentation is presented. A comparative study is made between the behavior of segmentation in color images using only the Euclidean metric of a* and b* and an adaptive color similarity function defined as a product of Gaussian functions in a modified HSI color space. For the evaluation synthetic images were particularly designed to accurately assess the performance of the color segmentation. The testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. From the results is obtained that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume.

Automatic Defect Detection and Grading of Single-Color Fruits Using HSV （Hue, Saturation, Value） Color Space

Machine vision has been recently utilized for quality control of food and agricultural products, which was traditionally done by manual inspection. The present study was an attempt for automatic defect detection and sorting of some single-color fruits such as banana and plum. Fruit images were captured using a color digital camera with capturing direction of zero degree and under illuminant D65. It was observed that growing decay and time-aging made surface color changes in bruised parts of the object. 3D RGB and HSV color vectors as well as a single channel like H （hue）, S （saturation）, V （value） and grey scale images were applied for color quantization of the object. Results showed that there was a distinct threshold in the histogram of the S channel of images which can be applied to separate the object from its background. Moreover, the color change via the defect and time-aging is correctly distinguishable in the hue channel image. The effect of illumination, gloss and shadow of 3D image processing is less noticeable for hue data in comparison to saturation and value. The value of H channel was quantized to five groups based on the difference between each pixel value and the H value of a healthy object. The percentage of different degree of defects can be computed and used for grading the fruits.

Circle Detection Based on HSI Color Space

A method based on HSI color space is presented to solve the problem of circle detection from color images. In terms of the evaluation to the edge detection method based on intensity, the edge detection based on hue is chosen to process the color image, and the simplified calculation of hue transform is discussed. Then the algorithm of circle detection based on Canny edge detection is proposed. Due to the dispersive distribution of the detected result, Hough transformation and template smooth are used in circle detection, and the proposed method gives a quite good result.

Modified Multispectral Space for Color Representation

Conventional multispectral space for color representation is modified by algorithm of cluster analysis and principal component analysis. Results show that the newer modified multispectral space not only keeping the structure of the original space but eliminating zero crosses as well its accuracy of color representation is higher than the conventional.

Energy-Saving Effect of Discolored Decorative Boards in Flexible Interior Space

Green,energy conservation and environmental protection have increasingly become the theme of the sustained and healthy development of cities against the background of new urbanization,which indicates that the problem of building energy consumption has received growing attention.This paper explores the impact of energy-saving decorations in flexible interior space on energy-saving effect of buildings so as to broaden the horizon of energy conservation in building,thereby alleviating the problem of energy shortage in China.

Combination of effective color information and machine learning for rapid prediction of soil water content

Soil water content(SWC)is one of the critical indicators in various fields such as geotechnical engineering and agriculture.To avoid the time-consuming,destructive,and laborious drawbacks of conventional SWC measurements,the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis.In this study,a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images,aiming to alleviate the interference of complex surface conditions with color information extraction.In addition,an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images.Meanwhile,a subset of 10 parameters is identified through three variable analytical methods.Then,four machine learning models for SWC prediction based on partial least squares regression(PLSR),random forest(RF),support vector machines regression(SVMR),and Gaussian process regression(GPR),are established using 32 and 10 characteristic parameters,and their performance is compared.The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions.The RGB,CIEXYZ,and CIELCH color spaces and lightness parameters,as the inputs,are more suitable for the SWC prediction models.Furthermore,it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy,and the GPR model has the best prediction performance(R^(2)≥0.95,RMSE≤2.01%,RPD≥4.95,and RPIQ≥6.37).The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction,providing valuable insights for mapping soil moisture fields.

分裂的δ-Jordan李color三系的结构

研究了分裂的δ-Jordan李color三系的结构,定义了分裂的δ-Jordan李color三系的概念,利用分裂的δ-Jordan李color三系的根连通的性质,得到具有对称根系的分裂的δ-Jordan李color三系的分解:T=U+∑[α]∈Λ1/~I[α],其中U是T0的子空间,I[α]是T的理想,并且满足如果[α]≠[β],那么{I[α],T,I[β]}={I[α],I[β],T}={T,I[α],I[β]}=0。

结合主动光源和改进YOLOv5s模型的夜间柑橘检测方法

【目的】解决夜间环境下遮挡和较小柑橘难以准确识别的问题,实现采摘机器人全天候智能化作业。【方法】提出一种结合主动光源的夜间柑橘识别方法。首先,通过分析主动光源下颜色特征不同的夜间柑橘图像,选择最佳的光源色并进行图像采集。然后,提出一种夜间柑橘检测模型BI-YOLOv5s,该模型采用双向特征金字塔网络(Bi-FPN)进行多尺度交叉连接和加权特征融合,提高对遮挡和较小果实的识别能力;引入Coordinate attention(CA)注意力机制模块,进一步加强对目标位置信息的提取;采用融入Transformer结构的C3TR模块,在减少计算量的同时更好地提取全局信息。【结果】本文提出的BI-YOLOv5s模型在测试集上的精准率、召回率、平均准确率分别为93.4%、92.2%和97.1%,相比YOLOv5s模型分别提升了3.2、1.5和2.3个百分点。在所采用的光源色环境下,模型对夜间柑橘识别的正确率为95.3%,相比白光环境下提高了10.4个百分点。【结论】本文提出的方法对夜间环境下遮挡和小目标柑橘的识别具有较高的准确性,可为夜间果蔬智能化采摘的视觉精准识别提供技术支持。

基于包装材料数字图像的RGB颜色空间色差评价方法研究

目的解决包装材料复杂颜色区域色差评价,目测检验方法存在判定标准复杂、结果一致性差等问题。方法提出一种基于包装材料高清数字图像的RGB颜色空间色差评价方法。通过搭建高清数字图像采集装置,获取包装材料标准样(上限、中限、下限)的全幅数字图像。通过采用RGB颜色空间降维的方法,对标准样测试区域的颜色空间进行离散边界拟合,构建标准样品测试区域的三限样RGB色差模型,与测试样对应测试区域的颜色空间进行对比;根据颜色空间模型的一致性判定测试样色差是否合格。结果对比多种离散边界拟合模型,采用滚球法边缘拟合模型的包含率和多出率分别达到100%、78.9%,模型拟合效果最佳。通过对包装材料色差缺陷样验证测试,采用上述色差评价方法准确率达到100%。结论本文提出的滚球法颜色空间边缘拟合模型和色差评价方法,可实现对包装材料复杂颜色区域的色差合格性判定,有利于提高企业生产的智能化检测水平。

基于分数阶全变分和低秩正则化的彩色图像去模糊方法

针对现有的彩色图像去模糊过程中存在色彩失衡、阶梯效应和伪影等现象,提出了一种基于分数阶全变分和低秩正则的图像去模糊优化方法。首先,将传统的RGB彩色图像转换到YCbCr颜色空间,利用其亮度通道特征解决色彩失衡问题;其次,利用分数阶全变分的特征消除图像恢复任务中出现的阶梯效应,并且引入加权核范数低秩正则进一步抑制伪影及噪声;最后,利用交替方向乘子法设计出高效的求解方法,通过迭代优化得到纯净图像的最优估计。对彩色图像测试的实验结果表明,所提出的方法对图像去模糊任务取得较好的视觉恢复效果,客观评价指标良好。

基于Raspberry Pi的安全帽识别系统设计与实现

为了便于施工危险区域人员的自动化识别,提出了一种基于Raspberry Pi的安全帽识别算法。该算法将摄像头采集到的原始视频图像进行滤波、形态学等处理,再对图像中的安全帽进行识别。对于彩色安全帽,将原始图像转换到HSV空间,根据不同颜色色调阈值的设定同时识别红、黄、蓝三种颜色的安全帽,并结合形状特征剔除错误目标。对于白色安全帽,将原始图像转化成B通道下的灰度图像,解决了将黄色误检为白色的问题。采用V通道直方图均衡化的方法,提升了昏暗光线条件下的图像亮度。实验结果表明:在无需提前训练的情况下,算法对于单色安全帽识别准确率超过了91%,对于多色安全帽识别率超过了90%,为施工危险区域的安全隐患排查和作业管控提供了解决方案。

国内空间色彩研究进展——基于CiteSpace的可视化分析

在城乡一体化趋势下,空间色彩研究持续完善,但研究格局失衡,亟须完善空间色彩体系。文章搭建空间色彩研究框架,借助CiteSpace知识图谱软件,从宏观、中观、微观空间视角,检索CNKI有关色彩的学术论文。研究表明:①宏观、中观、微观色彩研究的主流主题分别为色彩景观、传统建筑色彩、夜景照明色彩;②空间色彩当前处于波动发展阶段,发展动力不足;③热点演进受时代背景的影响,研究内容趋于综合化,研究方法趋于科学定量化。