Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Context-Dependent Attention System for a Social Robot
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Object-based visual attention for computer vision
Artificial Intelligence
A Principled Approach to Detecting Surprising Events in Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Compound effects of top-down and bottom-up influences on visual attention during action recognition
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Environment adapted active multi-focal vision system for object detection
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A high-speed multi-GPU implementation of bottom-up attention using CUDA
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Navigation through urban environments by visual perception and interaction
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Autonomous switching of top-down and bottom-up attention selection for vision guided mobile robots
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A model of dynamic visual attention for object tracking in natural image sequences
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Integrating context-free and context-dependent attentional mechanisms for gestural object reference
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Attentive object detection using an information theoretic saliency measure
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, autonomous switching between two basic attention selection mechanisms, i.e., top-down and bottom-up, is proposed. This approach fills a gap in object search using conventional top-down biased bottom-up attention selection, which fails, if a group of objects is searched whose appearances cannot be uniquely described by low-level features used in bottom-up computational models. Three internal robot states, such as observing, operating, and exploring, are included to determine the visual selection behavior. A vision-guided mobile robot equipped with an active stereo camera is used to demonstrate our strategy and evaluate the performance experimentally. This approach facilitates adaptations of visual behavior to different internal robot states and benefits further development toward cognitive visual perception in the robotics domain.