Fast semi-supervised segmentation of in-situ tree color images

Philippe Borianne 1 Gérard Subsol 2
2 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Résumé : In this paper we present an original semi-supervised method for the segmentation of in situ tree color images which combines color quantization, adaptive fragmentation of learning areas defined by the human operator and labeling propagation. A mathematical morphology post-processing is introduced to emphasize the narrow and thin structures which characterize branches. Applied in the L*a*b* color system, this method is well adapted to easily adjust the learning set so that the resultant labeling corresponds to the accuracy achieved by the human operator. The method has been embarked and evaluated on a tablet to help tree professionals in their expertise or diagnosis. The images, acquired and processed with a mobile device, present more or less complex background both in terms of content and lightness, more or less dense foliage and more or less thick branches. Results are good on images with soft lightness without direct sunlight.
Type de document :
Communication dans un congrès
ICISP: International Conference on Image and Signal Processing, Jun 2014, Cherbourg, France. Springer, 6th International Conference on Image and Signal Processing, LNCS (8509), pp.161-172, 2014, Image and Signal Processing. 〈10.1007/978-3-319-07998-1_19〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01381829
Contributeur : Gérard Subsol <>
Soumis le : vendredi 14 octobre 2016 - 18:00:25
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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Philippe Borianne, Gérard Subsol. Fast semi-supervised segmentation of in-situ tree color images. ICISP: International Conference on Image and Signal Processing, Jun 2014, Cherbourg, France. Springer, 6th International Conference on Image and Signal Processing, LNCS (8509), pp.161-172, 2014, Image and Signal Processing. 〈10.1007/978-3-319-07998-1_19〉. 〈lirmm-01381829〉

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