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

By: Schauerte, Boris [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Cognitive Systems Monographs: 30Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XXIV, 203 p. 55 illus., 51 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319337968.Subject(s): Engineering | Artificial intelligence | Image processing | Pattern recognition | Computational intelligence | Robotics | Automation | Engineering | Computational Intelligence | Robotics and Automation | Artificial Intelligence (incl. Robotics) | Image Processing and Computer Vision | Pattern RecognitionAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
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. .
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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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