Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Autonomous Attentive Exploration in Search and Rescue Scenarios
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Qualitative vision-based path following
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
Biologically inspired mobile robot vision localization
IEEE Transactions on Robotics
A saliency map method with cortex-like mechanisms and sparse representation
Proceedings of the ACM International Conference on Image and Video Retrieval
Learning pre-attentive driving behaviour from holistic visual features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Information Sciences: an International Journal
Multi-scale gist feature manifold for building recognition
Neurocomputing
Accelerators for biologically-inspired attention and recognition
Proceedings of the 50th Annual Design Automation Conference
Robotics and Autonomous Systems
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We introduce a method for rapidly classifying visual scenes globally along a small number of navigationally relevant dimensions: depth of scene, presence of obstacles, path versus nonpath, and orientation of path. We show that the algorithm reliably classifies scenes in terms of these high-level features, based on global or coarsely localized spectral analysis analogous to early-stage biological vision. We use this analysis to implement a real-time visual navigational system on a mobile robot, trained online by a human operator. We demonstrate successful training and subsequent autonomous path following for two different outdoor environments, a running track and a concrete trail. Our success with this technique suggests a general applicability to autonomous robot navigation in a variety of environments.