Multimodal Computational Attention for Scene Understanding and Robotics
Multimodal Computational Attention for Scene Understanding and Robotics
Springer | Engineering | June 9 2016 | ISBN-10: 3319337947 | 203 pages | pdf | 9.28 mb
Springer | Engineering | June 9 2016 | ISBN-10: 3319337947 | 203 pages | pdf | 9.28 mb
Authors: Schauerte, Boris
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
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
Number of Illustrations and Tables
4 b/w illustrations, 51 illustrations in colour
Topics
Computational Intelligence
Robotics and Automation
Artificial Intelligence (incl. Robotics)
Image Processing and Computer Vision
Pattern Recognition
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