Valerio Giuffrida

valerio giuffrida

Dr Valerio Giuffrida

Lecturer

Biography

Valerio obtained his PhD in Image Analysis at IMT School for Advanced Studies Lucca (Italy) in the 2018, with a thesis entitled “Learning to Count Leaves of Plants”. He has been engaged in several projects related to non-destructive image-based plant phenotyping. He proposed the first award-winning machine learning approach to predict the number of leaves of rosette plants, presented at Computer Vision Problems in Plant Phenotyping Workshop held in conjunction with the BMVC 2015. This algorithm was also included in the Phenotiki Analysis Software (http://phenotiki.com), software also developed in collaboration with him and has been downloaded more than 100 times. His expertise includes deep learning, publishing several papers related to deep leaf counting [27], [30], plant image synthesis [31], and domain adaptation applied plant phenotyping [4]. Several of these works were the first real applications in plant phenotyping. He also collaborated for the BBSRC project BB/P023487/1 as PDRA at the University of Edinburgh, for which he developed the Phenotiki Sensors and the analysis algorithms to extract root system architecture (RSA) traits.

His research interests are computer vision, machine learning, and deep learning applied to plant phenotyping and medical imaging applications. He also focuses in explainability and interpretability of deep networks, and transfer learning (domain adaptation).

Research Areas

Esteem

Invited Speaker

  • Big Data in Society Meeting
  • Camera Trap Symposium
  • Computer Vision for Agriculture
  • Presentation of the paper 'Explicit Factorization of Rotations in Restricted Boltzmann Machines'
  • Generating Images using Neural Networks

 

Date


20 results

CAPE: Context-Aware Private Embeddings for Private Language Learning

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. Howe...

Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap

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. T...

Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses

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 devel...

Towards Continuous User Authentication Using Personalised Touch-Based Behaviour

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 e...

Affordable and robust phenotyping framework to analyse root system architecture of soil-grown plants

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/10.1111/tpj.14877
The phenotypic analysis of root system growth is important to inform efforts to enhance plant resource acquisition from soils. However, root phenotyping still remains challeng...

Understanding Deep Neural Networks For Regression In Leaf Counting

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', du...

Doing More With Less: A Multitask Deep Learning Approach in Plant Phenotyping

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, 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 pla...

Unsupervised Rotation Factorization in Restricted Boltzmann Machines

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) mode...

Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation

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 di...

Adversarial Large-scale Root Gap Inpainting

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-fil...

Current Post Grad projects