Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated location matching in movies
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
A fast probabilistic model for hypothesis rejection in SIFT-Based object recognition
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Improving SIFT-Based object recognition for robot applications
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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As the RoboCup leagues evolve, higher requirements (e.g. object recognition skills) are imposed over the robot vision systems, which cannot be fulfilled using simple mechanisms as pure color segmentation or visual sonar. In this context the main objective of this article is to propose a robust object recognition system, based on the wide-baseline matching between a reference image (object model) and a test image where the object is searched. The wide baseline matching is implemented using local interest points and invariant descriptors. The proposed object recognition system is validated in two real-world tasks, recognition of objects in the RoboCup @Home league, and detection of robots in the humanoid league.