Multimodal Computational Attention for Scene Understanding and Robotics

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Schauerte, Boris (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edición:1st ed. 2016.
Colección:Cognitive Systems Monographs, 30
Temas:
Computational intelligence.
Control engineering.
Robotics.
Automation.
Artificial intelligence.
Computer vision.
Pattern recognition systems.
Computational Intelligence.
Control, Robotics, Automation.
Artificial Intelligence.
Computer Vision.
Automated Pattern Recognition.
Acceso en línea:Texto Completo

MARC

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300 |a XXIV, 203 p. 55 illus., 51 illus. in color.  |b online resource. 
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490 1 |a Cognitive Systems Monographs,  |x 1867-4933 ;  |v 30 
505 0 |a Introduction -- Background -- Bottom-up Audio-Visual Attention for Scene Exploration -- Multimodal Attention with Top-Down Guidance -- Conclusion -- Applications -- Dataset Overview. 
520 |a 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. . 
650 0 |a Computational intelligence. 
650 0 |a Control engineering. 
650 0 |a Robotics. 
650 0 |a Automation. 
650 0 |a Artificial intelligence. 
650 0 |a Computer vision. 
650 0 |a Pattern recognition systems. 
650 1 4 |a Computational Intelligence. 
650 2 4 |a Control, Robotics, Automation. 
650 2 4 |a Artificial Intelligence. 
650 2 4 |a Computer Vision. 
650 2 4 |a Automated Pattern Recognition. 
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830 0 |a Cognitive Systems Monographs,  |x 1867-4933 ;  |v 30 
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950 |a Engineering (R0) (SpringerNature-43712) 

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