All Outputs (22)

CAPE: Context-Aware Private Embeddings for Private Language Learning (2021)
Presentation / Conference Contribution
Plant, R., Gkatzia, D., & Giuffrida, V. (2021). CAPE: Context-Aware Private Embeddings for Private Language Learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (7970-7978)

Neural language models have contributed to state-of-the-art results in a number of downstream applications including sentiment analysis, intent classification and others. However, obtaining text representations or embeddings using these models risks... Read More about CAPE: Context-Aware Private Embeddings for Private Language Learning.

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/jimaging7100198

This 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.

Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses (2021)
Presentation / Conference Contribution
Frangulea, M., Pantos, C., Giuffrida, V., & Valente, J. (2021). Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses. In Precision agriculture ’21 (493-500). https://doi.org/10.3920/978-9

A tool for plant phenotyping is proposed to aid users in analyzing data on-demand. This tool is web-based and runs deep learning models. The current study focuses on the development of this tool, as well as obtaining a plant dataset to train a neural... Read More about Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses.

Towards Continuous User Authentication Using Personalised Touch-Based Behaviour (2020)
Presentation / Conference Contribution
Aaby, P., Giuffrida, M. V., Buchanan, W. J., & Tan, Z. (2020). Towards Continuous User Authentication Using Personalised Touch-Based Behaviour. In 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and

In this paper, we present an empirical evaluation of 30 features used in touch-based continuous authentication. It is essential to identify the most significant features for each user, as behaviour is different amongst humans. Thus, a fixed feature s... Read More about Towards Continuous User Authentication Using Personalised Touch-Based Behaviour.

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.

Understanding Deep Neural Networks For Regression In Leaf Counting (2020)
Presentation / Conference Contribution
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2020). Understanding Deep Neural Networks For Regression In Leaf Counting. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (4321-4329). https://doi.org/10.1109/C

Deep learning methods are constantly increasing in popularity and success across a wide range of computer vision applications. However, they are perceived as 'black boxes', due to the lack of an intuitive interpretation of their decision processes. W... Read More about Understanding Deep Neural Networks For Regression In Leaf Counting.

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.2020.00141

Image-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.2019.2946455

Finding 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.

Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation (2019)
Presentation / Conference Contribution
Giuffrida, M. V., Dobrescu, A., Doerner, P., & Tsaftaris, S. A. (2019). Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation.

Deep learning is making strides in plant phenotyping and agriculture. But pretrained models require significant adaptation to work on new target datasets originating from a different experiment even on the same species. The current solution is to ret... Read More about Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation.

Adversarial Large-scale Root Gap Inpainting (2019)
Presentation / Conference Contribution
Chen, H., Giuffrida, M. V., Doerner, P., & Tsaftaris, S. A. (2019). Adversarial Large-scale Root Gap Inpainting.

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 segmentin... Read More about Adversarial Large-scale Root Gap Inpainting.

Root Gap Correction with a Deep Inpainting Model (2018)
Presentation / Conference Contribution
Chen, H., Giuffrida, M. V., Doerner, P., & Tsaftaris, S. A. (2018). Root Gap Correction with a Deep Inpainting Model.

Imaging roots of growing plants in a non-invasive and affordable fashion has been a long-standing problem in image-assisted plant breeding and phenotyping. One of the most affordable and diffuse approaches is the use of mesocosms, where plants are gr... Read More about Root Gap Correction with a Deep Inpainting Model.

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.14064

Direct 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/s13007-018-0278-7

Background: 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.

Leveraging multiple datasets for deep leaf counting (2018)
Presentation / Conference Contribution
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2017, October). Leveraging multiple datasets for deep leaf counting. Presented at 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy

The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on l... Read More about Leveraging multiple datasets for deep leaf counting.

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.2017.2764326

We 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.

ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network (2017)
Presentation / Conference Contribution
Giuffrida, M. V., Scharr, H., & Tsaftaris, S. A. (2017, October). ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network. Presented at 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy

In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems , such as leaf segmentation (a multi-instance problem) and counting. Most of... Read More about ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network.

Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants (2017)
Journal Article
Minervini, M., Giuffrida, M. V., Perata, P., & Tsaftaris, S. A. (2017). Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants. Plant Journal, 90(1), 204-216. https://doi.org/10.1111/tpj.

Phenotyping is important to understand plant biology, but current solutions are costly, not versatile or are difficult to deploy. To solve this problem, we present Phenotiki, an affordable system for plant phenotyping that, relying on off-the-shelf p... Read More about Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants.

Whole Image Synthesis Using a Deep Encoder-Decoder Network (2016)
Presentation / Conference Contribution
Sevetlidis, V., Giuffrida, M. V., & Tsaftaris, S. A. (2016, October). Whole Image Synthesis Using a Deep Encoder-Decoder Network. Presented at SASHIMI Workshop - MICCAI, Athens, Greece

The synthesis of medical images is an intensity transformation of a given modality in a way that represents an acquisition with a different modality (in the context of MRI this represents the synthesis of images originating from different MR sequence... Read More about Whole Image Synthesis Using a Deep Encoder-Decoder Network.

On Blind Source Camera Identification (2015)
Presentation / Conference Contribution
Farinella, G. M., Giuffrida, M. V., Digiacomo, V., & Battiato, S. (2015). On Blind Source Camera Identification. In Advanced Concepts for Intelligent Vision Systems (464-473). https://doi.org/10.1007/978-3-319-25903-1_40

An interesting and challenging problem in digital image forensics is the identification of the device used to acquire an image. Although the source imaging device can be retrieved exploiting the file's header e.g., EXIF, this information can be easil... Read More about On Blind Source Camera Identification.

An interactive tool for semi-automated leaf annotation (2015)
Presentation / Conference Contribution
Minervini, M., Giuffrida, M. V., & Tsaftaris, S. (2015). An interactive tool for semi-automated leaf annotation. In Proceedings of the Computer Vision Problems in Plant Phenotyping Workshop 2015 (6.1-6.13). https://doi.org/10.5244/c.29.cvppp.6

High throughput plant phenotyping is emerging as a necessary step towards meeting agricultural demands of the future. Central to its success is the development of robust computer vision algorithms that analyze images and extract phenotyping informati... Read More about An interactive tool for semi-automated leaf annotation.