Visual properties that primarily attract bottom-up attention are collectively referred to as saliency. In this study, to understand the neural activity involved in top-down and bottom-up visual attention, we aim to prepare pairs of natural and unnatural images with common saliency. For this purpose, we propose an image transformation method based on deep neural networks that can generate new images while maintaining the consistent saliency map.
Although The most existing stochastic image generation methods focus on adding diversity of the overall style information, we developed a new image transformation method that makes the generated images look unnatural with high diversity of local image structures. We also conducted human experiments using our natural and corresponding unnatural images to measure overt eye movements and functional magnetic resonance imaging, and found that those images induced distinctive neural activities related to top-down and bottom-up attentional processing.
・ブレイン・マシン・インターフェース技術を用いた、医療・家電・娯楽などの分野
・注意の欠如による建設作業や運転時の事故予防などの危機管理サポートへの応用
| 氏名 | コース | 研究室 | 役職/学年 |
|---|---|---|---|
| 藤本啓介 | システム科学コース | 生命論理学分野 石井研究室 | 博士2回生 |