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Multimodal Computational Attention for Scene Understanding and Robotics [electronic resource] / by Boris Schauerte.

By: Contributor(s): Series: Cognitive Systems Monographs ; 30Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XXIV, 203 p. 55 illus., 51 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319337968
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q342
Online resources:
Contents:
Introduction -- Background -- Bottom-up Audio-Visual Attention for Scene Exploration -- Multimodal Attention with Top-Down Guidance -- Conclusion -- Applications -- Dataset Overview.
In: Springer eBooksSummary: 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. .
Item type: eBooks
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Introduction -- Background -- Bottom-up Audio-Visual Attention for Scene Exploration -- Multimodal Attention with Top-Down Guidance -- Conclusion -- Applications -- Dataset Overview.

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. .

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