Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Active fixation for scene exploration
International Journal of Computer Vision - Special issue: machine vision research at the Royal Institute of Technology
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Real-time visual attention on a massively parallel SIMD architecture
Real-Time Imaging
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
MAPS: multiscale attention-based presegmentation of color images
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Attention Selection with Self-supervised Competition Neural Network and Its Applications in Robot
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Autonomous Attentive Exploration in Search and Rescue Scenarios
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Multi-cue based place learning for mobile robot navigation
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
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Visual attention refers to the ability of a vision system to rapidly detect visually salient locations in a given scene. On the other hand, the selection of robust visual landmarks of an environment represents a cornerstone of reliable vision-based robot navigation systems. Indeed, can salient scene locations provided by visual attention be useful for robot navigation? This work investigates the potential and effectiveness of the visual attention mechanism to provide pre-attentive scene information to a robot navigation system. The basic idea is to detect and track the salient locations, or spots of attention by building trajectories that memorize the spatial and temporal evolution of these spots. Then, a persistency test, which is based on the examination of the lengths of built trajectories, allows the selection of good environment landmarks. The selected landmarks can be used for feature-based localization and mapping systems which helps mobile robot to accomplish navigation tasks.