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Multimodal computational attention for scene understanding and robotics
This book presents state-of-the-art computational attention models that have been successfully tested in diverse application areas and can build the foundation for artificial systems to efficiently explore, analyze, and understand natural scenes. It gives a comprehensive overview of the most recent...
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Lenguaje: | eng |
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Springer
2016
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Acceso en línea: | https://dx.doi.org/10.1007/978-3-319-33796-8 http://cds.cern.ch/record/2157650 |
_version_ | 1780950727155253248 |
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author | Schauerte, Boris |
author_facet | Schauerte, Boris |
author_sort | Schauerte, Boris |
collection | CERN |
description | This book presents state-of-the-art computational attention models that have been successfully tested in diverse application areas and can build the foundation for artificial systems to efficiently explore, analyze, and understand natural scenes. It gives a comprehensive overview of the most recent computational attention models for processing visual and acoustic input. It covers the biological background of visual and auditory attention, as well as bottom-up and top-down attentional mechanisms and discusses various applications. In the first part new approaches for bottom-up visual and acoustic saliency models are presented and applied to the task of audio-visual scene exploration of a robot. In the second part the influence of top-down cues for attention modeling is investigated. . |
id | cern-2157650 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2016 |
publisher | Springer |
record_format | invenio |
spelling | cern-21576502021-04-21T19:40:57Zdoi:10.1007/978-3-319-33796-8http://cds.cern.ch/record/2157650engSchauerte, BorisMultimodal computational attention for scene understanding and roboticsEngineeringThis book presents state-of-the-art computational attention models that have been successfully tested in diverse application areas and can build the foundation for artificial systems to efficiently explore, analyze, and understand natural scenes. It gives a comprehensive overview of the most recent computational attention models for processing visual and acoustic input. It covers the biological background of visual and auditory attention, as well as bottom-up and top-down attentional mechanisms and discusses various applications. In the first part new approaches for bottom-up visual and acoustic saliency models are presented and applied to the task of audio-visual scene exploration of a robot. In the second part the influence of top-down cues for attention modeling is investigated. .Springeroai:cds.cern.ch:21576502016 |
spellingShingle | Engineering Schauerte, Boris Multimodal computational attention for scene understanding and robotics |
title | Multimodal computational attention for scene understanding and robotics |
title_full | Multimodal computational attention for scene understanding and robotics |
title_fullStr | Multimodal computational attention for scene understanding and robotics |
title_full_unstemmed | Multimodal computational attention for scene understanding and robotics |
title_short | Multimodal computational attention for scene understanding and robotics |
title_sort | multimodal computational attention for scene understanding and robotics |
topic | Engineering |
url | https://dx.doi.org/10.1007/978-3-319-33796-8 http://cds.cern.ch/record/2157650 |
work_keys_str_mv | AT schauerteboris multimodalcomputationalattentionforsceneunderstandingandrobotics |