Exploring the Association between Climate Change and Human Development: A Visual Analytics Study

This study explores the complex relationship between climate change and human development. The aim is to understand how climate change affects human development across countries, regions, and the global population. Visual analytics were used to examine the impact of various climate change indicators on different aspects of human development. The study highlights the urgent need for climate change action and encourages policymakers to make decisive moves. Climate change adversely affects numerous aspects of daily life, leading to significant consequences that must be addressed through policy changes and global governance recommendations. Key findings include that regions with higher CO2 emissions experience a significantly higher incidence of life-threatening diseases compared to regions with lower emissions. Additionally, higher CO2 emissions correlate with consistent death rates. Increased pollution exposure is associated with a higher prevalence of life-threatening diseases and higher rates of malnutrition. Moreover, greater mineral depletion is linked to more frequent life-threatening diseases, suggesting that industrialization contributes to adverse health effects. These results provide valuable insights for policy and decision-making aimed at mitigating the impact of climate change on human development.

Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features

Visual odometry is critical in visual simultaneous localization and mapping for robot navigation.However,the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight.Herein,a new human visual attention mechanism for point-and-line stereo visual odometry,which is called point-line-weight-mechanism visual odometry(PLWM-VO),is proposed to describe scene features in a global and balanced manner.A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism,where sufficient attention is assigned to position-distinctive objects(sparse features in the environment).Furthermore,the sum of absolute differences algorithm is used to improve the accuracy of initialization for line features.Compared with the state-of-the-art method(ORB-VO),PLWM-VO show a 36.79%reduction in the absolute trajectory error on the Kitti and Euroc datasets.Although the time consumption of PLWM-VO is higher than that of ORB-VO,online test results indicate that PLWM-VO satisfies the real-time demand.The proposed algorithm not only significantly promotes the environmental adaptability of visual odometry,but also quantitatively demonstrates the superiority of the human visual attention mechanism.

Intelligent 3D garment system of the human body based on deep spiking neural network

Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and interaction.Methods To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent garments.The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and sensors.Additionally,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion.This system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other fields.The accurate classification of human actions contributes to the development of personalized training plans and injury prevention strategies.Conclusions This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin visualization.The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.

Evaluating Methods of Visual Assistance for Workers to Improve Quality and Usability in Individualized Kitchen Cabinet Assembly

In times of digitalisation, visual assistance systems in assembly are increasingly important. The design of these assembly systems needs to be highly complex to meet the requirements. Due to the increasing number of variants in production processes, as well as shorter innovation and product life cycles, assistance systems should improve quality and reduce complexity of assembly processes. However, many large kitchen manufacturers still assemble kitchen cabinets manually, due to the high variety of components, such as rails and fittings. This paper focuses on the analysis and evaluation of virtual assistance systems to improve quality and usability in individualised kitchen cabinet assembly processes at a large German manufacturer. A solution is identified and detailed.

基于混沌和HVS的小波域自适应图像水印算法

针对常规的小波域水印算法自适应性差的缺陷,提出了一种新的基于混沌和人类视觉系统的小波域自适应图像水印算法。首先对载体图像进行二级DWT变换;然后对特定的中频子带进行分块,并对每块做SVD分解;最后将Logistic混沌置乱后的二值水印信息嵌入到每块的最大奇异值中,嵌入的强度根据HVS原理自适应地进行调整。仿真实验表明,该算法不仅具有很好的不可见性,且对JPEG压缩、加噪、滤波等常见的图像攻击具有很强的鲁棒性。

结合HVS和相似特征的图像质量评估方法

图像质量的客观评价方法研究在实现图像质量评价仪器化的过程中起到决定性的作用。在分析最新全参考图像质量评价算法:特征相似法(feature similarity,FSIM)的基础上,利用对比敏感度函数(contrast sensitivity function,CSF)算子以及离散余弦变换(discrete cosine transform,DCT)域的对比度掩盖效应,提出了一种改进的FSIM图像质量评价方法。该方法具有FSIM算法简单、高效等特性,同时又充分体现人眼视觉特性,更好地反映了人的主观感受。LIVE(laboratory for image and video engi-neering)测试数据集的实验结果证明,该方法在非线性回归后相关系数、斯皮尔曼相关系数、线外率等指标方面均优于传统的其他图像质量评价算法。

基于神经网络和HVS的空域数字水印算法

为了弥补空间域水印算法在提取水印时需要原宿主图像、鲁棒性较差等缺点,提出一种基于BP神经网络和人眼视觉特性(HVS)的空域数字水印算法。先选出原宿主图像对比度函数值最大的前块用于二值水印图像的嵌入,利用神经网络能够逼近任意非线性关系的特点和其自适应性,构建水印信号嵌入前后图像块像素值间的映射关系,实现了水印的盲提取,并将该算法与现有算法进行了对比。实验结果表明,对于JPEG压缩、加噪、剪切和旋转等常见的图像处理攻击,算法具有良好的鲁棒性和不可见性。

基于HVS的小波变换数字图像水印算法

为实现图像水印在数据认证、访问控制和版权保护等领域发挥作用,提出一种人眼视觉感知特性的小波变换数字图像水印算法。首先对二值图像水印进行Arnold变换,并采用行堆叠的方法,转换成一维序列,然后根据密钥生成的高斯序列中值的正负来确定水印信息嵌入时是采用那种调制方式,最后对原始图像进行3级DWT分解,在小波图像的第二层中频子带中,结合人眼视觉特性将水印正向或负向调制叠加在宿主图像大于JND值的小波系数上。实验结果表明:该算法能抵抗JPEG压缩、加噪、剪切等多种攻击,具有良好的透明性,鲁棒性和安全性。

HVS模型及其在静止图象压缩质量评价中的应用

图象质量尺度是最优化图象压缩算法参数和提高图象质量的重要依据 .建立在人类视觉模型 (HVS)基础之上的感知质量尺度作为主客观联系的桥梁 ,能有效地反映出人对图象失真在视觉上的感知 .近年来 ,有许多研究者借助人类视觉系统研究中的最新成果 ,深入分析了与图象质量密切相关的视觉感知特性 ,提出了大量效果不错的静止图象压缩感知质量尺度 ,并对视觉感知特性在图象质量尺度中的应用方法进行了较全面的综述 ,揭示了影响其图象质量预测准确性、鲁棒性的主要因素 ,给出了该领域的最新研究成果和未来发展方向 .

一种HVS和Contourlet结合的图像水印算法

提出了一种Contourlet域的图像水印算法.首先对图像进行多层分解;然后利用视觉系统的亮度纹理特性,把伪随机序列组成的水印信号自适应地嵌入到能量最大的子带中;最后经过逆变换得到嵌入水印后的图像.该算法解决了直接嵌入边缘扭曲的问题,检测端使用基于相关的盲检测.实验结果表明,该算法能够在满足不可见性的条件下抵抗JPEG压缩、加噪和剪裁等攻击,具有很强的鲁棒性.

基于HVS的小波图像融合新算法

目前的大多数基于小波变换的图像融合算法 ,由于并没有细致考虑人眼的视觉特性 ,因而得到的融合效果有时并不理想。为了提高图像的融合效果 ,提出了一种新的基于人类视觉系统 (HVS)的小波图像融合新算法。该算法充分考虑了人类视觉系统的特点 ,首先在多尺度小波分解的基础上 ,计算出每层每个像素邻域的纹理和边缘亮度相关量 ;然后自适应地求得加权融合系数 ;最后采用多层次迭代法来产生融合图像。实验结果表明 ,该算法不仅能使图像融合结果有较大的改善 ,而且有利于人眼对目标的探测和识别。

基于多分辨率和HVS的盲图像水印隐藏方法

文章提出了一种盲图像水印隐藏方法，结合了人类视觉系统（ ＨＶＳ）和图像的 ＤＷＴ多分辨分解，实验结果表明这个方法是有效的。

基于HVS特性的图像自适应中值滤波算法

通过考虑宿主图像亮度、纹理、边缘等特征,提出一种图像自适应中值滤波算法。该算法利用基于人眼视觉特性的临界噪声阈值确定噪声点,根据噪声密度自适应调整滤波窗口大小,采用改进的中值滤波对检测出的噪声点进行处理,从而在去除噪声的同时较好地保护图像细节。实验结果表明,该算法比传统中值滤波及其改进算法有更好的滤波性能,对于噪声污染严重的图像,滤波效果更好。

图像质量的HVS评价方法

在充分分析人类视觉系统(HVS)的亮度、纹理、边缘等特性,并给出相应的视觉特性计算模型后,提出一种结合HVS特性的加权峰值信噪比计算方法。实验结果表明该模型计算出的HVS值能充分反映人类视觉系统的屏蔽特性;该图像质量评价方法的评价结果与主观评价结果相一致,效果优于传统图像质量评价方法。

一种基于HVS的彩色图像水印算法

讨论了一种将彩色水印图像嵌入到原始彩色图像中的数字水印算法。它利用人眼视觉系统特性,采用了在DWT域下按照频带重要性来嵌入水印的规则,即:水印应当首先嵌入图像小波低频系数,若有剩余,再按图像小波频带重要性的排序嵌入高频带。实验结果表明,提取的水印表现出较好的鲁棒性,且主观视觉失真较小。

一种基于HVS的利用零树编码的水印算法

提出了一种利用人类视觉特性的数字图像水印算法 .先采取直接序列扩展方式生成水印序列信号 ,然后嵌入到一组按重要性选出的小波系数中 ,其中嵌入强度由小波域临界可见误差JND(JustNoticedDiffer ence)门限决定 .实验证明此算法在水印的不可见性和鲁棒性之间取得了良好的平衡 ,既保证了人眼视觉的不可察觉性 ,又具有较好的鲁棒性 .

基于HVS的彩色逆半调色差预测及可视化方法

提出了一种彩色逆半调色差预测及可视化评价方法.该方法从HVS特性着手,首先将图像变换到模式-颜色分离的S-CIELab空间,然后运用CIEDE2000色差公式和NBS色差感知参考值建立了彩色逆半调质量评价方案.仿真结果表明:该方法提高了色差计算的精度,拓展了色差表现形式,符合HVS特性,所生成的HVS色差图、色差分布、色差比率和色差均值能够直观、准确地反映半调噪声的抑制情况和重建图像质量.

一种基于图像分割和HVS的自适应数字水印

提出了一种基于图像分割和HVS的自适应数字水印算法。利用数字图像分割提取出图像中具有重要语义的区域，称为兴趣区域，作为水印嵌入的主要部位。每个兴趣区域按照其语义上的重要程度得到一个语义权值。对兴趣区域的子块，按照HVS特征给出子块的视觉屏蔽权值。两个权值的加权和决定各子块待嵌水印的强度。水印嵌入到各子块DCT域的低频部分。实验结果表明，该水印有很好的不可见性和鲁棒性。

基于HVS和量化的自适应盲水印算法

提出了一种基于HVS和量化的自适应盲水印算法,基于HVS的视觉模型使用亮度掩蔽系数,纹理掩蔽系数,频率掩蔽系数来自适应地调整水印的嵌入强度。算法选择在DCT域,通过用JPEG量化表量化DCT系数,比较量化后的DC系数|DC|与选定的量化后的AC系数|AC|之间的关系来嵌入和提取水印,并实现了水印的盲检测。由于DCT系数基于JPEG量化表来量化,对JPEG压缩具有很好的抵抗能力,对叠加噪声、模糊等常规的图像操作也具有很好的鲁棒性。实验结果证明,该算法是一种行之有效的方法。

结合HVS及SSIM的无参考模糊图像评价方法

针对传统无参考模糊图像质量评价算法未考虑人类视觉系统在模糊检测时的有限性这一缺点,提出了一种结合人类视觉特性及结构相似度的无参考图像质量评价方法。该方法向原始输入图像增加低通滤波器生成重新模糊图像;之后创建一个以原始输入图像的边界点为中心的强边界像素块集合;分别计算集合中像素块与对应的重新模糊图像中像素块间的结构相似度指数;所得的全部结构相似度指数的均值作为最终的原始输入图像的模糊值。基于LIVE模糊图像数据库的实验结果表明,该方法有较好的性能,其计算结果与主观统计值的线性相关系数及斯皮尔曼等级相关系数分别为0.9298及0.9318。