Abstract
Image segmentation is a challenging task in computer vision fields. In this paper, we aim to distinguish green peppers from large amounts of green leaves by using hyperspectral information. Our key aim is to design a novel optical filter to identify the bands where peppers differ substantially from green leaves. We design an optical filter as a learnable weight in front of an RGB filter with a fixed weight, and classify green peppers in an end-to-end manner. Our work consists of two stages. In the first stage, we obtain the optical filter parameters by training an optical filter and a small neural network simultaneously at the pixel level of hyperspectral data. In the second stage, we apply the learned optical filter and an RGB filter in a successive manner to a hyperspectral image to obtain an RGB image. Then we use a SegNet-based network to obtain better segmentation results at the image level. Our experimental results demonstrate that this two-stage method performs well for a small dataset and the optical filter helps to improve segmentation accuracy.
X. Liu and P. Wang—This work was done during an internship at Kochi University of Technology.
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Acknowledgments
This work was supported by Cabinet Office grant in aid, the Advanced Next-Generation Greenhouse Horticulture by IoP (Internet of Plants), Japan.
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Yu, J., Liu, X., Wang, P., Kurihara, T. (2020). Design of an Optical Filter to Improve Green Pepper Segmentation Using a Deep Neural Network. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_51
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