Visual attention modeling for 3D video using neural networks

Iana Iatsun, M. Larabi, C. Fernandez-Maloigne
{"title":"Visual attention modeling for 3D video using neural networks","authors":"Iana Iatsun, M. Larabi, C. Fernandez-Maloigne","doi":"10.1109/IC3D.2014.7032602","DOIUrl":null,"url":null,"abstract":"Visual attention is one of the most important mechanisms in the human visual perception. Recently, its modeling becomes a principal requirement for the optimization of the image processing systems. Numerous algorithms have already been designed for 2D saliency prediction. However, only few works can be found for 3D content. In this study, we propose a saliency model for stereoscopic 3D video. This algorithm extracts information from three dimensions of content, i.e. spatial, temporal and depth. This model benefits from the properties of interest points to be close to human fixations in order to build spatial salient features. Besides, as the perception of depth relies strongly on monocular cues, our model extracts the depth salient features using the pictorial depth sources. Since weights for fusion strategy are often selected in ad-hoc manner, in this work, we suggest to use a machine learning approach. The used artificial Neural Network allows to define adaptive weights based on the eye-tracking data. The results of the proposed algorithm are tested versus ground-truth information using the state-of-the-art techniques.","PeriodicalId":244221,"journal":{"name":"2014 International Conference on 3D Imaging (IC3D)","volume":"113 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on 3D Imaging (IC3D)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC3D.2014.7032602","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Visual attention is one of the most important mechanisms in the human visual perception. Recently, its modeling becomes a principal requirement for the optimization of the image processing systems. Numerous algorithms have already been designed for 2D saliency prediction. However, only few works can be found for 3D content. In this study, we propose a saliency model for stereoscopic 3D video. This algorithm extracts information from three dimensions of content, i.e. spatial, temporal and depth. This model benefits from the properties of interest points to be close to human fixations in order to build spatial salient features. Besides, as the perception of depth relies strongly on monocular cues, our model extracts the depth salient features using the pictorial depth sources. Since weights for fusion strategy are often selected in ad-hoc manner, in this work, we suggest to use a machine learning approach. The used artificial Neural Network allows to define adaptive weights based on the eye-tracking data. The results of the proposed algorithm are tested versus ground-truth information using the state-of-the-art techniques.
基于神经网络的三维视频视觉注意建模
视觉注意是人类视觉感知中最重要的机制之一。近年来,其建模已成为图像处理系统优化的主要要求。许多算法已经被设计用于二维显著性预测。然而,只有很少的作品可以找到3D内容。在这项研究中,我们提出了一个立体3D视频的显著性模型。该算法从内容的空间、时间和深度三个维度提取信息。该模型受益于兴趣点的属性,接近人类的注视点,以建立空间显著特征。此外,由于深度感知强烈依赖于单目线索,我们的模型使用图像深度源提取深度显著特征。由于融合策略的权重通常以特别的方式选择,在这项工作中,我们建议使用机器学习方法。所使用的人工神经网络允许基于眼动追踪数据定义自适应权重。使用最先进的技术对所提出的算法的结果与真实信息进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信