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

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.
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Contributor : Gérard Subsol Connect in order to contact the contributor
Submitted on : Friday, October 14, 2016 - 6:00:25 PM
Last modification on : Friday, August 5, 2022 - 3:02:19 PM

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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. pp.161-172, ⟨10.1007/978-3-319-07998-1_19⟩. ⟨lirmm-01381829⟩



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