Self-Supervised Leaf Segmentation under Complex Lighting Conditions

• URL: http://arxiv.org/abs/2203.15943v1
• Date: Tue, 29 Mar 2022 22:59:02 GMT
• Title: Self-Supervised Leaf Segmentation under Complex Lighting Conditions
• Authors: Xufeng Lin, Chang-Tsun Li, Scott Adams, Abbas Kouzani, Richard Jiang, Ligang He, Yongjian Hu, Michael Vernon, Egan Doeven, Lawrence Webb, Todd Mcclellan, Adam Guskic
• Abstract summary: Leaf segmentation is an essential prerequisite task in image-based plant phenotyping. We present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. Experimental results on datasets of different plant species demonstrate the potential of the proposed self-supervised framework.
• Score: 14.290827361756108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision tasks, its adaptation for image-based plant phenotyping remains rather unexplored. In this work, we present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. The self-supervised semantic segmentation model groups the semantically similar pixels by iteratively referring to the self-contained information, allowing the pixels of the same semantic object to be jointly considered by the color-based leaf segmentation algorithm for identifying the leaf regions. Additionally, we propose to use a self-supervised color correction model for images taken under complex illumination conditions. Experimental results on datasets of different plant species demonstrate the potential of the proposed self-supervised framework in achieving effective and generalizable leaf segmentation.

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