Multimodal Computational Attention for Scene Understanding and Robotics / by Boris Schauerte
By: Schauerte, Boris.
Contributor(s): SpringerLink (Online service)
Material type:
E-bookSeries: Cognitive Systems Monographs; 3030Publisher: Cham : Springer International Publishing, 2016Description: 1 recurso en línea (XXIV, 203 p.) : 55 ilustraciones, 51 ilustraciones en color.ISBN: 9783319337968.Subject: Inteligencia artificial
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds | |
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LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | TJ211.3 S338 2016 EB (Browse shelf(Opens below)) | .i1161481x | Acceso electrónico | eBOOK .i1161481x |
Browsing Madrid Digital shelves, Shelving location: Acceso Electrónico (UEM) Close shelf browser (Hides shelf browser)
| TJ211.3 2022 EB Robotic Vision : Fundamental Algorithms in MATLAB® | TJ211.3 2022 EB All Weather Robot Vision | TJ211.3 N493 2015 EB New Development in Robot Vision | TJ211.3 S338 2016 EB Multimodal Computational Attention for Scene Understanding and Robotics | TJ211.35 2018 EB Nonlinear Circuits and Systems for Neuro-inspired Robot Control | TJ211.35 2019 EB Visual guidance of unmanned aerial manipulators | TJ211.35 2019 EB Cognitive Reasoning for Compliant Robot Manipulation |
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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