Adversarial Large-Scale Root Gap Inpainting

Hao Chen, Mario Valerio Giuffrida, Peter Doerner, Sotirios A. Tsaftaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Root imaging of a growing plant in a non-invasive, affordable, and effective way remains challenging. One approach is to image roots by growing them in a rhizobox, a soil-filled transparent container, imaging them with digital cameras, and segmenting root from soil background. However, due to soil occlusion and the fact that digital imaging is a 2D projection of a 3D object, gaps are present on the segmentation masks, which may hinder the extraction of finely grained root system architecture (RSA) traits. Herein, we develop an image inpainting technique to recover gaps from disconnected root segments. We train a patch-based deep fully convolutional network using a supervised loss but also use adversarial mechanisms at patch and whole root level. We use Policy Gradient method, to endow the model with large-scale whole root view during training. We train our model using synthetic root data. In our experiments, we show that using adversarial mechanisms at local and whole-root level we obtain a 72% improvement in performance on recovering gaps of real chickpea data when using only patch-level supervision.
Original languageUndefined/Unknown
Title of host publicationThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
PublisherComputer Vision Foundation
Publication statusPublished - 20 Jun 2019
Event2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition - Long Beach, United States
Duration: 16 Jun 201920 Jun 2019


Conference2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2019
CountryUnited States
CityLong Beach
Internet address

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