Segmentation Algorithm for Machine-Harvested Cotton based on S and I Regional Features

Lei Li, Chengliang Zhang, Xinyu Zheng

2019

Abstract

A segmentation method based on regional color information is proposed for the complicated natural impurities in machine-harvested cotton. The color gradient operation of the filtered machine cotton picking image is carried out, and the marked image is obtained by extended minimum transformation operation. The initial segmented image is obtained by using the watershed algorithm on the modified gradient image. Spatial proximity and color information are considered comprehensively in the process of region merging. Saturation S and brightness I as color information feature are mainly used in the paper. In order to make the algorithm more accurately, the information features are updated in the process of merging. The experimental results show that the average segmentation accuracy of the method for natural impurities is 92%.

Paper Citation

in Harvard Style

Li L., Zhang C. and Zheng X. (2019). Segmentation Algorithm for Machine-Harvested Cotton based on S and I Regional Features.In Proceedings of 5th International Conference on Vehicle, Mechanical and Electrical Engineering - Volume 1: ICVMEE, ISBN 978-989-758-412-1, pages 347-353. DOI: 10.5220/0008850503470353

in Bibtex Style

@conference{icvmee19,
author={Lei Li and Chengliang Zhang and Xinyu Zheng},
title={Segmentation Algorithm for Machine-Harvested Cotton based on S and I Regional Features},
booktitle={Proceedings of 5th International Conference on Vehicle, Mechanical and Electrical Engineering - Volume 1: ICVMEE,},
year={2019},
pages={347-353},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008850503470353},
isbn={978-989-758-412-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of 5th International Conference on Vehicle, Mechanical and Electrical Engineering - Volume 1: ICVMEE,
TI - Segmentation Algorithm for Machine-Harvested Cotton based on S and I Regional Features
SN - 978-989-758-412-1
AU - Li L.
AU - Zhang C.
AU - Zheng X.
PY - 2019
SP - 347
EP - 353
DO - 10.5220/0008850503470353