Multimodal Computational Attention for Scene Understanding and Robotics
Nonfiction, Science & Nature, Technology, Robotics, Computers, Advanced Computing, Artificial Intelligence, General Computing
| Author: | Boris Schauerte | ISBN: | 9783319337968 |
| Publisher: | Springer International Publishing | Publication: | May 11, 2016 |
| Imprint: | Springer | Language: | English |
| Author: | Boris Schauerte |
| ISBN: | 9783319337968 |
| Publisher: | Springer International Publishing |
| Publication: | May 11, 2016 |
| Imprint: | Springer |
| Language: | English |
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.
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.
