Non supervised perceptual model for target recognition in UAVs

Abstract : Today, drones play an interesting role in the so-called Revolution 4.0. One of the problems studied by various companies and research groups are the precision landing techniques since this drone feature can be used in applications such as package delivery or object tracking. In this paper, we propose a non-supervised model that allows to detect and recognize a set of landing targets using the Gestalt principles. This proposed method is capable to recognize different coded landing targets in a robust way under outdoor non-controlled light conditions. Comparing to thresholding techniques and other methods, this work deals with image degradations caused by shadows, change of scale, noise and camera target deformation.
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Communication dans un congrès
Reconnaissance des Formes, Image, Apprentissage et Perception RFIAP, Jun 2018, Marne la Vallée, France
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https://hal-enpc.archives-ouvertes.fr/hal-01790867
Contributeur : Eva Dokladalova <>
Soumis le : lundi 14 mai 2018 - 10:09:32
Dernière modification le : mercredi 30 mai 2018 - 15:42:59

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  • HAL Id : hal-01790867, version 1

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Eric Bazan, Petr Dokládal, Eva Dokladalova. Non supervised perceptual model for target recognition in UAVs. Reconnaissance des Formes, Image, Apprentissage et Perception RFIAP, Jun 2018, Marne la Vallée, France. 〈hal-01790867〉

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