Fuzzy c-means based plant segmentation with distance dependent threshold

Mads Dyrmann

Abstract

An important element in weed control using machine vision is the ability to be able to identify plant species based on shape. For this to be done, it is often necessary to segment the plants from the soil. This may cause problems if the colour of a plant is not consistent, since the plants are at risk of being separated into several objects. This study presents a plant segmentation method based on fuzzy c-means and a distance transform. This segmentation method is compared with four other plant segmentation methods based on various parameters, including the ability to maintain the plants as whole, connected components. The method presented here is found to be better at preserving plants as connected objects while keeping the false positive rate low.

Session

Workshop: Computer Vision Problems in Plant Phenotyping (CVPPP 2015)

Files

PDF iconPaper (PDF, 7M)

DOI

10.5244/C.29.CVPPP.5
https://dx.doi.org/10.5244/C.29.CVPPP.5

Citation

Mads Dyrmann. Fuzzy c-means based plant segmentation with distance dependent threshold. In S. A. Tsaftaris, H. Scharr, and T. Pridmore, editors, Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP), pages 5.1-5.11. BMVA Press, September 2015.

Bibtex

@inproceedings{CVPP2015_5,
	title={Fuzzy c-means based plant segmentation with distance dependent threshold},
	author={Mads Dyrmann},
	year={2015},
	month={September},
	pages={5.1-5.11},
	articleno={5},
	numpages={11},
	booktitle={Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP)},
	publisher={BMVA Press},
	editor={S. A. Tsaftaris, H. Scharr, and T. Pridmore},
	doi={10.5244/C.29.CVPPP.5},
	isbn={1-901725-55-3},
	url={https://dx.doi.org/10.5244/C.29.CVPPP.5}
}