Multimodal Computational Attention for Scene Understanding and Robotics (Cognitive Systems Monographs, 30)
Language: English
Published by Springer, 2018
- Softcover
- New

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- Title
- Multimodal Computational Attention for Scene Understanding and Robotics (Cognitive Systems Monographs, 30)
- Author
- Schauerte, Boris
- Publisher
- Springer
- Publication year
- 2018
- Condition
- New
- Book Type
- book
- Binding
- Paperback
- Language
- English
- ISBN 10
- 3319816055
- ISBN 13
- 9783319816050
- Series
- Book 18 of 31: Cognitive Systems Monographs
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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From the Back Cover
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.
"About the title" may belong to another edition of this title.
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