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近日,国际智能交通领域的学术期刊《IEEE Transactions on Intelligent Transportation Systems》在线发表了由长安大学运输工程学院房建武副教授、硕士生严定鑫等共同完成的学术论文:“事故场景中的驾驶注意预测”(DADA: Driver Attention Prediction in Driving Accident Scenarios,DOI:10.1...
Changes made during a brief visual interruption sometimes go undetected, even when the object undergoing the change is at the center of the observer's interest and spatial attention (Simons & Levin,...
Attention acts, through cortical feedback pathways, to enhance the response of cells encoding expected or predicted information. Such observations are inconsistent with the predictive coding theory of...
This paper proposes a novel face recognitionalgorithm inspired by the selective attention of Human VisualSystem (HVS). We record four observers’ eye movements whenthey are viewing 100 FRGC [1] frontal...
In this paper, we propose an attention based method to provide convenient browsing experience on mobile devices. To achieve this, we extract video summary in both temporal and spatial domains. Firstly...
This paper presents a robust video fingerprinting based on visual attention regions. Video fingerprints, which are a set of short feature vectors, are unique to video clips and used for video identifi...
From visual perception viewpoint, actions in videos can capture high-level semantics for video content understanding and retrieval. However, action-level video retrieval meets great challenges, due ...
This paper presents a generic Virtual Content Insertion (VCI) system based on visual attention analysis. VCI is an emerging application of video analysis and has been used in video augmentation and a...
Thispaper presents the notion of Contextualized Attention Metadata (CAM) inlearning environments. CAM describes observations about the handling ofdigital information in relation to the context in whic...
In this paper, by considering the multiple spatial-temporal characteristic of visual perception system, we propose a novel home video attention analysis method. Firstly, each frame of the video is seg...
In this paper, we propose a visual-aural attention modeling based video content analysis approach, which can be used to automatically detect the highlights of the popular TV program—talk show video. F...

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