Environmentally adaptive crop extraction for agricultural automation using super-pixel and LAB Gaussian model

Cuina Li; Guangyu Shi; Zhenghong Yu

doi:10.1117/12.2285490

8 March 2018 Environmentally adaptive crop extraction for agricultural automation using super-pixel and LAB Gaussian model

Cuina Li, Guangyu Shi, Zhenghong Yu

Author Affiliations +

Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 1060914 (2018) https://doi.org/10.1117/12.2285490
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

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.

Citation Download Citation

Cuina Li, Guangyu Shi, and Zhenghong Yu "Environmentally adaptive crop extraction for agricultural automation using super-pixel and LAB Gaussian model", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060914 (8 March 2018); https://doi.org/10.1117/12.2285490

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