通过基于视觉的多模态界面增强情境感知
Towards Enhanced Context Awareness with Vision-based Multimodal Interfaces
AI总结：
该博士研究针对人机交互中基于视觉的界面情境感知提升需求，结合多模态AI技术，从尺度、深度、时间三个视觉维度探索三类多模态界面应用，以实现更流畅高效的人机交互。
摘要：
基于视觉的界面（Vision-based Interfaces, VIs）是推动人机交互（Human-Computer Interaction, HCI）发展的关键，尤其在提升情境感知方面作用显著。然而，随着多模态人工智能（Artificial Intelligence, AI）的快速发展，这类界面仍存在巨大的发展空间，有望实现人类与智能系统的深度耦合。由AI驱动的VIs与其他模态融合后，可提供可靠的解决方案，有效捕捉并解读用户意图与复杂环境信息，从而实现流畅高效的交互。本博士研究探索了三类用于增强情境感知的多模态界面应用案例，分别对应视觉模态的三个维度：尺度、深度与时间，具体包括通过显微图像对物理表面进行细粒度分析、利用深度数据实现真实世界的精准投影，以及在虚拟环境中基于视频背景生成触觉反馈。
英文摘要：
Vision-based Interfaces (VIs) are pivotal in advancing Human-Computer Interaction (HCI), particularly in enhancing context awareness. However, there are significant opportunities for these interfaces due to rapid advancements in multimodal Artificial Intelligence (AI), which promise a future of tight coupling between humans and intelligent systems. AI-driven VIs, when integrated with other modalities, offer a robust solution for effectively capturing and interpreting user intentions and complex environmental information, thereby facilitating seamless and efficient interactions. This PhD study explores three application cases of multimodal interfaces to augment context awareness, respectively focusing on three dimensions of visual modality: scale, depth, and time: a fine-grained analysis of physical surfaces via microscopic image, precise projection of the real world using depth data, and rendering haptic feedback from video background in virtual environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
