Simple Gabor feature space for invariant object recognition
Pattern Recognition Letters
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
COLD: The CoSy Localization Database
International Journal of Robotics Research
Multi-cue discriminative place recognition
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Cue integration through discriminative accumulation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An extended-HCT semantic description for visual place recognition
International Journal of Robotics Research
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A Bayesian approach for place recognition
Robotics and Autonomous Systems
A visual attention-based approach for automatic landmark selection and recognition
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Fast scene change detection using direct feature extraction fromMPEG compressed videos
IEEE Transactions on Multimedia
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Place recognition is important navigation ability for autonomous navigation of mobile robots. Visual cues extracted from images provide a way to represent and recognize visited places. In this article, a multi-cue based place learning algorithm is proposed. The algorithm has been evaluated on a localization image database containing different variations of scenes under different weather conditions taken by moving the robot-mounted camera in an indoor-environment. The results suggest that joining the features obtained from different cues provide better representation than using a single feature cue.