No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Training visual attention in a naturalistic visual environment

The efficiency of training visual attention in the central and peripheral visual field was investigated by means of a visual detection task that was performed in a naturalistic visual environment including numerous, time-varying visual distractors. We investigated the minimum number of repetitions of the training required to obtain the top performance and whether intra-day training improved performance as efficiently as inter-day training. Additionally, our research aimed to find out whether exposure to a demanding task such as a microsurgical intervention may cancel out the effects of training.

Results showed that performance in visual attention peaked within three (for tasks in the central visual field) to seven (for tasks in the periphery) days subsequent to training. Intra-day training had no significant effect on performance. When attention training was administered after exposure to stress, improvement of attentional performance was more pronounced than when training was completed before the exposure. Our findings support the implementation of training in situ at work for more efficient results.

Practitioner Summary: Visual attention is important in an increasing number of workplaces, such as with surveillance, inspection, or driving. This study shows that it is possible to train visual attention efficiently within three to seven days. Because our study was executed in a naturalistic environment, training results are more likely to reflect the effects in the real workplace.     

基于深度卷积神经网络的视觉里程计研究

视觉里程计利用视频信息来估计相机运动的位姿参数，实现对智能体的定位。传统视觉里程计方法需要特征提取、特征匹配/跟踪、外点剔除、运动估计、优化等流程，解算非常复杂，因此，提出了基于卷积神经网络的方法来实现端对端的单目视觉里程计。借助卷积神经网络对彩色图片自动学习提取图像帧间变化的全局特征，将用于分类的卷积神经网络转化为帧间时序特征网络，通过三层全连接层输出相机的帧间相对位姿参数。在KITTI数据集上的实验结果表明，提出的Deep-CNN-VO模型可以较准确地估计车辆的运动轨迹，证明了方法的可行性。在简化了复杂模型的基础上，与传统的视觉里程计系统相比，该模型的精度也有所提高。     

乙酰丙酮-目视判读法快速测定白酒中甲醇的含量

目的建立了乙酰丙酮-目视判读法快速测定白酒中甲醇的方法。方法采用乙酰丙酮法,白酒中甲醇经高锰酸钾氧化,草酸还原过量高猛酸钾后,在沸水浴中与乙酰丙酮发生反应,并对分析条件进行了探讨优化,对酒精度影响和干扰因素进行了深入研究并提出了解决办法。结果对优化后的分析条件进行了多个实验室间验证。本快速检验方法灵敏度为100%,特异性为≥94%,假阴性为0%,假阳性率为≤6.0%,相对准确度≥98.5%,满足要求。结论该方法灵敏、准确、快速,适用于白酒中甲醇的含量的现场快速检测。     

舞台灯光对服饰视觉色感的影响研究

在舞台表演中,舞台灯光对人物服饰色彩的视觉体验有很大影响。文章基于光源、布料光谱信息和CIE-1931色度模型,模拟出计算面料色彩推测公式。并选取三种不同色调的LED光源作为舞台灯光,分别对七种面料进行色彩分析,发现深长春花蓝色、中蓝色面料在三种灯光下的色相变化不明显,与太阳光下呈现色彩接近;丁香紫色和兰花紫色面料与灯光色彩倾向一致;金黄色、橙红色和兰花紫色三种颜色在蓝光强烈情况下的色彩视认性更强。最后,结合舞台色彩的视认性和色彩的意象,为舞台服装设计与灯光设计在视觉色感的选择提供帮助。     

基于机器视觉的机车齿轮毂全自动双轴拧紧机的设计和应用

设计了基于机器视觉的机车齿轮毂全自动双轴拧紧机,根据拧紧工艺和扭矩控制原理实现对螺栓拧紧力矩的控制;通过丝杠变径机构,实现不同规格齿轮毂螺栓拧紧;利用机器视觉实现螺栓位置的识别以及初始位置的确定;讨论了自动拧紧机控制系统的硬件组成和软件系统配置,并介绍了防错、防漏功能的设计流程。螺栓自动拧紧机可提高装配精度和操作效率,降低工人的劳动强度。     

主/次显示器视距设计对头部关节舒适度的影响

以视平线可与显示器中心重合为准,分别探讨显示器中心在视平线上方、下方时不同视距对头部关节舒适度的影响. 实验自变量为视距(眼睛与显示器中心的水平距离),因变量为头部关节舒适度. 实验结果显示:1)显示器中心在人视平线上方时,头部舒适性状态始终处于不舒适状态,此时视距与人的头部舒适性关系成正比,即视距越大,头部舒适性越好;2)显示器中心在人视平线下方时,头部关节舒适状态始终处于正常范围,舒适性程度随视距增大而减小.     

Multi-modal approach to evaluate adaptive visual stimuli of remote operation system using gaze behavior

A multi-modal approach is proposed to evaluate the usability of Adaptive Visual Stimuli for User Interface (AVS4UI) of remote operation systems. This study focuses on the evaluation of AVS4UI for forklift work because the operation complexity includes driving and cargo handling, which typically requires multiple salient attention. Presenting this amount of information simultaneously on a User Interface (UI) tends to cause confusions to operators and reduces operation efficiency. AVS4UI can therefore be one of the promising solutions where the optimal visual stimuli are autonomously presented for different work conditions. However, evaluation of AVS4UI is challenging because operators may be disoriented by adaptive information and worked without safety considerations. Therefore, novel gaze metrics are proposed to evaluate responses of forklift operators to AVS4UI so that undesired behavior can be evaluated. The proposed metrics implicitly represent gaze pattern in terms of transition and distribution between UI elements, operation safety, and familiarity with the adaptive system. The ideal AVS4UI is expected to minimize the proposed gaze metrics and outperform the non-adaptive UI. More importantly, the results of these metrics are consistent with those of perceived workload defined by NASA-Task Load Index. We also propose a correlation model using stepwise linear regression that provides reasonable estimation of perceived workload. Such novel metrics and correlation model enable objective and online evaluation to minimize biases of subjective response. Therefore, online work support system can be developed to support workers.     

基于文本与语义相关性分析的图像检索

为了更加有效地检索到符合用户复杂语义需求的图像，提出一种基于文本描述与语义相关性分析的图像检索算法。该方法将图像检索分为两步：基于文本语义相关性分析的图像检索和基于SIFT特征的相似图像扩展检索。根据自然语言处理技术分析得到用户文本需求中的关键词及其语义关联，在选定图像库中通过语义相关性分析得到“种子”图像；接下来在图像扩展检索中，采用基于SIFT特征的相似图像检索，利用之前得到的“种子”图像作为查询条件，在网络图像库中进行扩展检索，并在结果集上根据两次检索的图像相似度进行排序输出，最终得到更加丰富有效的图像检索结果。为了证明算法的有效性，在标准数据集Corel5K和网络数据集Deriantart8K上完成了多组实验，实验结果证明该方法能够得到较为精确地符合用户语义要求的图像检索结果，并且通过扩展算法可以得到更加丰富的检索结果。     

空间语义增强下的城市交通事故数据可视分析

目的 海量城市交通事故数据可能蕴含有交通事故的空间模式，挖掘出交通事故的空间模式有助于开展交通事故的防治工作。目前交通管理部门虽然记录了交通事故发生地的空间位置信息，但没有对事故发生地进行空间语义描述，从而影响对交通事故空间模式的深入分析。因此，提出一种交通事故数据空间语义增强方法，并设计了一套可视分析系统。方法 基于城市兴趣点来增强交通事故数据的空间语义。以事故发生点为中心获取周围城市兴趣点，使用特征向量刻画兴趣点的数量、类别及其与事故发生点的距离，并称此向量为空间语义特征向量。将空间语义特征向量和相应的交通事故关联，以达到增强其空间语义的目的。然后，基于空间语义特征向量，使用自组织映射聚类算法对交通事故进行聚类分析，根据其空间语义特征将交通事故分为若干类别。最后，通过使用地图视图展示事故点数据、聚类视图和平行坐标视图展示聚类分析的结果及其空间语义特征的可视化方法，对交通事故的空间模式进行分析。结果 针对空间语义增强的交通事故数据以及相关分析任务，有效地使用上述数据分析方法与可视化技术，设计并实现了一套多视图关联的可视分析系统，提供了便捷的交互方式辅助用户分析。通过研发人员和交通警察共同对安徽省合肥市2018年的交通事故数据进行分析，将交通事故发生地划分9类并指出每类地点的空间语义特点，进一步分析出了事故高发区域的空间语义特性。结论 本文提出的交通事故数据空间语义增强方法和可视分析方法可以帮助用户揭示交通事故的空间语义模式，有助于深入分析和认识交通事故的成因，能为交通事故防治相关的城市建设工作提供建议。