Overview
- Recent research on Multimodal Computational Attention for Scene Understanding
- Presents a combined auditory and visual saliency in a biologically-plausible model implemented on a humanoid robot’s head
- Describes a series of behavioral experiments, which show that the presented model exhibits the desired behaviors
- Includes supplementary material: sn.pub/extras
Part of the book series: Cognitive Systems Monographs (COSMOS, volume 30)
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Table of contents (5 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Multimodal Computational Attention for Scene Understanding and Robotics
Authors: Boris Schauerte
Series Title: Cognitive Systems Monographs
DOI: https://doi.org/10.1007/978-3-319-33796-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-33794-4Published: 20 May 2016
Softcover ISBN: 978-3-319-81605-0Published: 27 May 2018
eBook ISBN: 978-3-319-33796-8Published: 11 May 2016
Series ISSN: 1867-4925
Series E-ISSN: 1867-4933
Edition Number: 1
Number of Pages: XXIV, 203
Number of Illustrations: 4 b/w illustrations, 51 illustrations in colour
Topics: Computational Intelligence, Robotics and Automation, Artificial Intelligence, Image Processing and Computer Vision, Pattern Recognition