NDLI: Spatial discrimination in task-driven attention

Content Provider	IEEE Xplore Digital Library
Author	Belardinelli, A. Pirri, F. Carbone, A.
Copyright Year	2006
Abstract	Visual attention is becoming an increasingly imperative capability to endow computer vision systems and autonomous agents with. Starting from a biological inspired model of attention, we present an experiment aimed to study selective attention in 3D space. Depth has been proved to be an important feature affecting the way attention is deployed when observing a scene. We studied preferential scanning paths and fixation zones in a task-driven wandering of the tutor gaze over a scene where multiple targets had been disposed on different depth planes. We supposed that selective attention would aggregate targets in cliques that maximize utility, minimizing meanwhile visual effort produced when passing from closer planes to further planes or between different cluttered locations. By means of a purposely designed machine, we stored visual and motor data of the tutor's head; we clustered different scanning paths of the gaze shifts according to velocity and space criteria to determine a preference model of attentional shifts and fixations. We propose subsequently a utility model that can formalize acquired information and establish a vision-based attentional framework for robots. We show that an interpretation of task-driven gaze orienting based on the presented preference criteria correctly accounts for the studied behaviours, as further reported in the literature
Starting Page	321
Ending Page	327
File Size	1128481
Page Count	7
File Format	PDF
ISBN	1424405645
DOI	10.1109/ROMAN.2006.314437
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-06
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Layout Robot vision systems Orbital robotics Object recognition Focusing Computational modeling Human robot interaction Computer vision Autonomous agents Biological system modeling
Content Type	Text
Resource Type	Article

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