Multimodal computational attention for scene understanding and robotics [electronic resource] / Boris Schauerte.
2016
TJ211.3
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Details
Title
Multimodal computational attention for scene understanding and robotics [electronic resource] / Boris Schauerte.
Author
Schauerte, Boris, author.
ISBN
9783319337968 (electronic book)
3319337963 (electronic book)
9783319337944
3319337963 (electronic book)
9783319337944
Published
Switzerland : Springer, 2016.
Language
English
Description
1 online resource (xxiv, 203 pages) : illustrations.
Call Number
TJ211.3
Dewey Decimal Classification
629.8/92637
Summary
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. .
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed May 19, 2016).
Series
Cognitive systems monographs ; v. 30.
Available in Other Form
Multimodal Computational Attention for Scene Understanding and Robotics.
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Table of Contents
Introduction
Background
Bottom-up Audio-Visual Attention for Scene Exploration
Multimodal Attention with Top-Down Guidance
Conclusion
Applications
Dataset Overview.
Background
Bottom-up Audio-Visual Attention for Scene Exploration
Multimodal Attention with Top-Down Guidance
Conclusion
Applications
Dataset Overview.