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.     

儿童用品TBT通报数据可视化预警研究

探索采用数据可视化技术分析儿童用品TBT通报数据,以可视化图形图像呈现通报热点并揭示趋势信息,提出对策与建议,助力为儿童用品产业升级、TBT预警数据分析和信息传播工作提供新思路,提高中小企业的国外市场准入机会。     

Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator

Train driving is a highly visual task. The visual capabilities of the train driver affects driving safety and driving performance. Understanding the effects of train speed and background image complexity on the visual behavior of the high-speed train driver is essential for optimizing performance and safety. This study investigated the role of the apparent image velocity and complexity on the dynamic visual field of drivers. Participants in a repeated-measures experiment drove a train at nine different speeds in a state-of-the-art high-speed train simulator. Eye movement analysis indicated that the effect of image velocity on the dynamic visual field of high-speed train driver was significant while image complexity had no effect on it. The fixation range was increasingly concentrated on the middle of the track as the speed increased, meanwhile there was a logarithmic decline in fixation range for areas surrounding the track. The extent of the visual search field decreased gradually, both vertically and horizontally, as the speed of train increased, and the rate of decrease was more rapid in the vertical direction. A model is proposed that predicts the extent of this tunnel vision phenomenon as a function of the train speed.Relevance to industryThis finding can be used as a basis for the design of high-speed railway system and as a foundation for improving the operational procedures of high-speed train driver for safety.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

The Precedence Effect in Three Species of Birds (Melopsittacus undulatus, Serinus canaria, and Taeniopygia guttata).

The perceived locations of paired auditory images, simulating direct sounds and their echoes, have been recently studied in budgerigars (Melopsittacus undulatus; M. L. Dent & R. J. Dooling, 2003a, 2003b). In this article, the authors extend those experiments to include measurements of the precedence effect using a discrimination paradigm in two additional bird species: canaries (Serinus canaria) and zebra finches (Taeniopygia guttata). Although time courses of summing localization, localization dominance, and echo thresholds were similar across all species, budgerigars had slightly higher overall levels of discrimination. The results from these experiments add further support that the precedence effect in birds is similar to that found in other animals and that the ability to suppress echoes that might degrade localization and auditory object perception may be a general property of the vertebrate auditory system. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

从图像的二维谱分析现行电视的缺陷

从图像的二维谱可以得出现行广播电视图像为什么不能满足人眼的视觉匹配以及现行电视制度的不足 ,从而对未来的高清晰度电视发展提出要求。     

引入激光回馈的双光束干涉效应的研究

提出了一种引入He-Ne激光回馈的双光束干涉系统,并在理论和实验两方面进行了研究。实验中对系统中的干涉信号及激光器尾光功率变化同时进行探测。发现当干涉仪的主回馈镜移动时,激光器尾光信号是正弦形波形,而干涉仪输出的是以双峰为一个周期的信号,双峰中一峰总是高于另一峰,并且当主回馈镜移动方向改变时,同一周期中两峰出现的顺序也随之改变。对实验现象进行了理论分析,并模拟出干涉信号及激光自身功率的变化曲线。理论分析及模拟结果与实验结果完全吻合。讨论了利用发现的现象进行测量的可行性,所提出的测量方法易于实现。     

浅谈如何有效学习计算机软件知识

计算机软件知识的学习并不是完全线性的，而是不断循环往复、迭代的过程。本对如何深入、广泛、有效地学习计算机软件知识做出了一些探讨。