Proceedings Paper
Environmentally adaptive crop extraction for agricultural automation using super-pixel and LAB Gaussian model| Format | Member Price | Non-Member Price |
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Paper Abstract
In this paper, we proposed an environmentally adaptive crop extraction method for agricultural automation using LAB Gaussian model and super-pixel segmentation. A Gaussian mixture model in LAB color space is introduced to describe the distribution of crop pixel to adapt to the outdoor environment and the super-pixel technique is applied for structure preserving. Comparing experiment show that our method outperforms the other commonly used extraction methods.
Paper Details
Date Published: 8 March 2018
PDF: 6 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060914 (8 March 2018); doi: 10.1117/12.2285490
Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)
PDF: 6 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060914 (8 March 2018); doi: 10.1117/12.2285490
Show Author Affiliations
Cuina Li, Institute of Atmospheric Physics (China)
China Meteorological Administration (China)
Guangyu Shi, Institute of Atmospheric Physics (China)
China Meteorological Administration (China)
Guangyu Shi, Institute of Atmospheric Physics (China)
Zhenghong Yu, Guangdong Polytechnic of Science and Technology (China)
Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)
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