Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Class Recognition with Many Local Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Strategies for Object Manipulation using Foveal and Peripheral Vision
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Multiple Object Class Detection with a Generative Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
International Journal of Computer Vision
Learning Saccadic Gaze Control via Motion Prediciton
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
A non-myopic approach to visual search
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Using Multi-view Recognition and Meta-data Annotation to Guide a Robot's Attention
International Journal of Robotics Research
Foundations and Trends in Robotics
Saliency-Based Obstacle Detection and Ground-Plane Estimation for Off-Road Vehicles
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Simultaneous Visual Object Recognition and Position Estimation Using SIFT
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
Active multi-view object search on a humanoid head
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Visual place categorization: problem, dataset, and algorithm
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Accurate hardware-based stereo vision
Computer Vision and Image Understanding
Stereo matching using weighted dynamic programming on a single-direction four-connected tree
Computer Vision and Image Understanding
Robotic object detection: learning to improve the classifiers using sparse graphs for path planning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Topological spatial relations for active visual search
Robotics and Autonomous Systems
Quaternion-Based spectral saliency detection for eye fixation prediction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
A Computational Learning Theory of Active Object Recognition Under Uncertainty
International Journal of Computer Vision
Pattern Recognition Letters
A cognitive approach for robots' autonomous learning
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Inferring robot goals from violations of semantic knowledge
Robotics and Autonomous Systems
Robotics and Autonomous Systems
Object search using object co-occurrence relations derived from web content mining
Intelligent Service Robotics
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State-of-the-art methods have recently achieved impressive performance for recognising the objects present in large databases of pre-collected images. There has been much less focus on building embodied systems that recognise objects present in the real world. This paper describes an intelligent system that attempts to perform robust object recognition in a realistic scenario, where a mobile robot moving through an environment must use the images collected from its camera directly to recognise objects. To perform successful recognition in this scenario, we have chosen a combination of techniques including a peripheral-foveal vision system, an attention system combining bottom-up visual saliency with structure from stereo, and a localisation and mapping technique. The result is a highly capable object recognition system that can be easily trained to locate the objects of interest in an environment, and subsequently build a spatial-semantic map of the region. This capability has been demonstrated during the Semantic Robot Vision Challenge, and is further illustrated with a demonstration of semantic mapping. We also empirically verify that the attention system outperforms an undirected approach even with a significantly lower number of foveations.