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Learning an efficient and robust graph matching procedure for specific object recognition

Abstract : We present a fast and robust graph matching approach for 2D specific object recognition in images. From a small number of training images, a model graph of the object to learn is automatically built. It contains its local keypoints as well as their spatial proximity relationships. Training is based on a selection of the most efficient subgraphs using the mutual information. The detection uses dynamic programming with a lattice and thus is very fast. Experiments demonstrate that the proposed method outperforms the specific object detectors of the state-of-the-art in realistic noise conditions.
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https://hal.archives-ouvertes.fr/hal-01381492
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Submitted on : Friday, October 14, 2016 - 2:46:54 PM
Last modification on : Thursday, November 21, 2019 - 2:33:32 AM

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Jérôme Revaud, Guillaume Lavoué, Yasuo Ariki, Atilla Baskurt. Learning an efficient and robust graph matching procedure for specific object recognition. International Conference on Pattern Recognition (ICPR), Aug 2010, Istanbul, Turkey. pp.754-757, ⟨10.1109/ICPR.2010.190⟩. ⟨hal-01381492⟩

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