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Biologically Inspired Computational Models of Visual Attention for Personalized Autonomous Agents: A Survey

Biologically Inspired Computational Models of Visual Attention for Personalized Autonomous Agents: A Survey
학술지명 Lecture Notes in Electrical Engineering ISSN 1876-1100
SCI 유무 없음 게재연월 2012-01 Vol. 107 No. 1
표준화된 순위정보영향력지수 - IF - Citation -

Biologically Inspired Computational Models of Visual Attention for Personalized Autonomous Agents: A Survey
저자 JinYoung Moon, HyungGik Lee, ChangSeok Bae
초록
Perception is one of essential capabilities for personalized autonomous agents that act like their users without intervention of the users in order to understand the environment for themselves like a human being. Visual perception in humans plays a major role to interact with objects or entities within the environment by interpreting their visual sensing information. The major technical obstacle of visual perception is to efficiently process enormous amount of visual stimuli in real-time. Therefore, computational models of visual attention that decide where to focus in the scene have been proposed to reduce the visual processing load by mimicking human visual system. This article provides the background knowledge of cognitive theories that the models were founded on and analyzes the computational models necessary to build a personalized autonomous agent that acts like a specific person as well as typical human beings.
keyword Visual attention, Personalized, Autonomous agent

Biologically Inspired Computational Models of Visual Attention for Personalized Autonomous Agents: A Survey
과제명 인간 교감 신개념 UI 기반 인터랙션 기술
연구기관 한국전자통신연구원 연구책임자 손승원