Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object-based Visual Attention: a Model for a Behaving Robot
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Evaluation of Visual Attention Models for Robots
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Steerable wedge filters for local orientation analysis
IEEE Transactions on Image Processing
Autonomous Attentive Exploration in Search and Rescue Scenarios
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
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Visual attention is a crucial skill in human beings in that it allows optimal deployment of visual processing and memory resources. It turns out to be even more useful in search tasks, since to select salient zones we use top-down priors, depending on the observed scene, along with bottom-up criteria. In this paper we show how we constructed a robotic model of attention, inspired by studies on human attention and gaze shifting. Our model relies on a measure of salience related to the particular type of environment and to the given task. This measure is hierarchically structured and consists of both top-down components, learned from the tutor, and bottom-up components as perceived in the scene by the robot. Hence with such a general model the robot can perform its own scan-path inside a similar environment and report on its findings.