All Outputs (15)

TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication (2023)
Conference Proceeding
Aaby, P., Giuffrida, M. V., Buchanan, W. J., & Tan, Z. (in press). TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication.

We are increasingly required to prove our identity when using smartphones through explicit authentication processes such as passwords or physiological biometrics, e.g., authorising online banking transactions or unlocking smartphones. However, these... Read More about TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication.

CAPE: Context-Aware Private Embeddings for Private Language Learning (2021)
Conference Proceeding
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.

Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses (2021)
Conference Proceeding
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/10.3920/978-90-8686-916-9_59

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)
Conference Proceeding
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 Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00023

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.

Understanding Deep Neural Networks For Regression In Leaf Counting (2020)
Conference Proceeding
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/CVPRW.2019.00316

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.

Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation (2019)
Conference Proceeding
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)
Conference Proceeding
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)
Conference Proceeding
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.

Leveraging multiple datasets for deep leaf counting (2018)
Conference Proceeding
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2018). Leveraging multiple datasets for deep leaf counting. . https://doi.org/10.1109/ICCVW.2017.243

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.

ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network (2017)
Conference Proceeding
Giuffrida, M. V., Scharr, H., & Tsaftaris, S. A. (2017). ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network. . https://doi.org/10.1109/ICCVW.2017.242

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.

Whole Image Synthesis Using a Deep Encoder-Decoder Network (2016)
Conference Proceeding
Sevetlidis, V., Giuffrida, M. V., & Tsaftaris, S. A. (2016). Whole Image Synthesis Using a Deep Encoder-Decoder Network. In Simulation and Synthesis in Medical Imaging (127-137). https://doi.org/10.1007/978-3-319-46630-9_13

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)
Conference Proceeding
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)
Conference Proceeding
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.

Learning to Count Leaves in Rosette Plants (2015)
Conference Proceeding
Giuffrida, M. V., Minervini, M., & Tsaftaris, S. (2015). Learning to Count Leaves in Rosette Plants. In Proceedings of the Computer Vision Problems in Plant Phenotyping Workshop 2015. https://doi.org/10.5244/c.29.cvppp.1

Counting the number of leaves in plants is important for plant phenotyping, since it can be used to assess plant growth stages. We propose a learning-based approach for counting leaves in rosette (model) plants. We relate image-based descriptors lear... Read More about Learning to Count Leaves in Rosette Plants.

Exploiting time-multiplexing structured light with picoprojectors (2015)
Conference Proceeding
Giuffrida, M. V., Farinella, G. M., Battiato, S., & Guarnera, M. (2015). Exploiting time-multiplexing structured light with picoprojectors. In R. Sitnik, & W. Puech (Eds.), Three-Dimensional Image Processing, Measurement (3DIPM), and Applications 2015: Proceedings. https://doi.org/10.1117/12.2083031

When a picture is shot all the information about the distance between the object and the camera gets lost. Depth estimation from a single image is a notable issue in computer vision. In this work we present a hardware and software framework to accomp... Read More about Exploiting time-multiplexing structured light with picoprojectors.