Editorial: Synthetic data for computer vision in agriculture
(2023)
Journal Article
Afonso, M., & Giufrida, V. (2023). Editorial: Synthetic data for computer vision in agriculture. Frontiers in Plant Science, 14, Article 1277073. https://doi.org/10.3389/fpls.2023.1277073
Outputs (11)
Editorial: Computer vision in plant phenotyping and agriculture (2023)
Journal Article
Stavness, I., Giuffrida, V., & Scharr, H. (2023). Editorial: Computer vision in plant phenotyping and agriculture. Frontiers in Artificial Intelligence, 6, Article 1187301. https://doi.org/10.3389/frai.2023.1187301[Abstract not available.]
An omnidirectional approach to touch-based continuous authentication (2023)
Journal Article
Aaby, P., Giuffrida, M. V., Buchanan, W. J., & Tan, Z. (2023). An omnidirectional approach to touch-based continuous authentication. Computers and Security, 128, Article 103146. https://doi.org/10.1016/j.cose.2023.103146This paper focuses on how touch interactions on smartphones can provide a continuous user authentication service through behaviour captured by a touchscreen. While efforts are made to advance touch-based behavioural authentication, researchers often... Read More about An omnidirectional approach to touch-based continuous authentication.
Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap (2021)
Journal Article
Litrico, M., Battiato, S., Tsaftaris, S. A., & Giuffrida, M. V. (2021). Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap. Journal of Imaging, 7(10), Article 198. https://doi.org/10.3390/jimaging7100198This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x. The current literature generally lacks specific domain adaptation approaches... Read More about Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap.
Affordable and robust phenotyping framework to analyse root system architecture of soil-grown plants (2020)
Journal Article
Bontpart, T., Concha, C., Giuffrida, V., Robertson, I., Admkie, K., Degefu, T., …Doerner, P. (2020). Affordable and robust phenotyping framework to analyse root system architecture of soil-grown plants. Plant Journal, 103(6), 2330-2343. https://doi.org/The phenotypic analysis of root system growth is important to inform efforts to enhance plant resource acquisition from soils. However, root phenotyping still remains challenging due to soil opacity, requiring systems that facilitate root system visi... Read More about Affordable and robust phenotyping framework to analyse root system architecture of soil-grown plants.
Doing More With Less: A Multitask Deep Learning Approach in Plant Phenotyping (2020)
Journal Article
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2020). Doing More With Less: A Multitask Deep Learning Approach in Plant Phenotyping. Frontiers in Plant Science, 11, Article 141. https://doi.org/10.3389/fpls.2020.00141Image-based plant phenotyping has been steadily growing and this has steeply increased the need for more efficient image analysis techniques capable of evaluating multiple plant traits. Deep learning has shown its potential in a multitude of visual t... Read More about Doing More With Less: A Multitask Deep Learning Approach in Plant Phenotyping.
Unsupervised Rotation Factorization in Restricted Boltzmann Machines (2019)
Journal Article
Giuffrida, M. V., & Tsaftaris, S. A. (2020). Unsupervised Rotation Factorization in Restricted Boltzmann Machines. IEEE Transactions on Image Processing, 29(1), 2166-2175. https://doi.org/10.1109/TIP.2019.2946455Finding suitable image representations for the task at hand is critical in computer vision. Different approaches extending the original Restricted Boltzmann Machine (RBM) model have recently been proposed to offer rotation-invariant feature learning.... Read More about Unsupervised Rotation Factorization in Restricted Boltzmann Machines.
Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting (2018)
Journal Article
Giuffrida, M. V., Doerner, P., & Tsaftaris, S. A. (2018). Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting. Plant Journal, 96(4), 880-890. https://doi.org/10.1111/tpj.14064Direct observation of morphological plant traits is tedious and a bottleneck for high‐throughput phenotyping. Hence, interest in image‐based analysis is increasing, with the requirement for software that can reliably extract plant traits, such as lea... Read More about Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting.
Citizen crowds and experts: observer variability in image-based plant phenotyping (2018)
Journal Article
Giuffrida, M. V., Chen, F., Scharr, H., & Tsaftaris, S. A. (2018). Citizen crowds and experts: observer variability in image-based plant phenotyping. Plant Methods, 14(1), Article 12 (2018). https://doi.org/10.1186/s13007-018-0278-7Background: Image-based plant phenotyping has become a powerful tool in unravelling genotype–environment interactions. The utilization of image analysis and machine learning have become paramount in extracting data stemming from phenotyping experime... Read More about Citizen crowds and experts: observer variability in image-based plant phenotyping.
Multimodal MR Synthesis via Modality-Invariant Latent Representation (2017)
Journal Article
Chartsias, A., Joyce, T., Giuffrida, M. V., & Tsaftaris, S. A. (2018). Multimodal MR Synthesis via Modality-Invariant Latent Representation. IEEE Transactions on Medical Imaging, 37(3), 803-814. https://doi.org/10.1109/tmi.2017.2764326We propose a multi-input multi-output fully convolutional neural network model for MRI synthesis. The model is robust to missing data, as it benefits from, but does not require, additional input modalities. The model is trained end-to-end, and learns... Read More about Multimodal MR Synthesis via Modality-Invariant Latent Representation.