Reference : Effective segmentation of green vegetation for resource-constrained real-time applications
Scientific conferences in universities or research centers : Scientific conference in universities or research centers
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/184433
Effective segmentation of green vegetation for resource-constrained real-time applications
English
Krishna Moorthy Parvathi, Sruthi Moorthy mailto [Université de Liège > Ingénierie des biosystèmes (Biose) > Agriculture de précision >]
Boigelot, Bernard mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Informatique >]
Mercatoris, Benoît mailto [Université de Liège > Ingénierie des biosystèmes (Biose) > Agriculture de précision >]
15-Jul-2015
International
10th European Conference on Precision Agriculture
from 12-07-2015 to 16-07-2015
Victor Alchanatis and Yafit Cohen
Beit-Dagan
Israel
[en] Machine vision ; Vegetation segmentation ; HSV colour space
[en] This paper describes an improved algorithm for segmentation of green vegetation under uncontrolled illumination conditions and also suitable for resource-constrained real-time applications. The proposed algorithm uses a naïve Bayesian model to effectively combine various manually extracted features from two different color spaces namely RGB and HSV. The evaluation of 100 images indicated the better performance of the proposed algorithm than the vegetation index-based methods with comparable execution time. Moreover, the proposed algorithm performed better than the state-of-the-art EASA-based algorithms in terms of processing time and memory usage.
Biosystem Engineering Department, Gembloux Agro-Bio Tech and Montefiore Institute, Université de Liège
AgricultureIsLife of Gembloux Agro-Bio Tech, University of Liège, Belgium.
AgricultureIsLife
Researchers ; Students
http://hdl.handle.net/2268/184433

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