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That's me

My name is Valerio Giuffrida and I am in my last year of PhD at The Alan Turing Institute in London, UK, under the supervision of Dr Sotirios A Tsaftaris from the University of Edinburgh. I started my doctoral career in November 2014 at the IMT School For Advanced Studies Lucca and, since then, I have published several papers on machine learning and plant phenotyping. Specifically, my first publication presented a learning algorithm to count leaves in rosette plants. Then, I matured my research interests on neural networks, particularly on Restricted Boltzmann Machines (RBMs). Besides my scientific skills, I am an excellent programmer, boasting knowledge in several programming languages. In fact, I am the lead developer of the Phenotiki Analysis Software, which bundles computer vision and machine learning algorithms to analyse rosette plants. During my master, I had the possibility to participate at different scientific summer schools, such as the International Computer Vision Summer School (ICVSS) and the Medical Imaging Summer School (MISS), which have been of great motivations towards my scientific career.

News


CVPPP 2017

Mon, 17/07/2017

New Computer Vision Problems in Plant Phenotyping 2017 workshop, held in conjuction with ICCV 2017. More info here!

IPPN Workshop

Sat, 13/05/2017

I will hold a demo of Phenotiki at the IPPN Workshop 15-17 May at the Forschungszentrum Jülich. Click here for more info.

Zooniverse Project

Tue, 02/05/2017

Please get involved to our Zooniverse project here!

One Planet Meeting - Nairobi - 14-15 March

Mon, 20/03/2017

Group photo

Il posto giusto

Mon, 27/02/2017

Research Interests


Invariant Feature Learning
Invariant Feature Learning
Plant Phenotyping
Plant Phenotyping
Medical Imaging and Modality Synthesis
Medical Imaging and Modality Synthesis
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Invariant Feature Learning
Plant Phenotyping
Medical Imaging and Modality Synthesis

Publications


Phenotiki: An open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants
Massimo Minervini, Mario Valerio Giuffrida, Pierdomenico Perata, Sotirios A. Tsaftaris

Phenotyping is important to understand plant biology but current solutions are either costly, not versatile or difficult to deploy. To solve this problem, we present Phenotiki, an affordable system for plant phenotyping which, relying on off-the-shelf parts, provides an easy to install and maintain platform, offering an out-of-box experience for a well established phenotyping need: imaging rosette-shaped plants. The accompanying software (with available source code) processes data originating from our device seamlessly and automatically. Our software relies on machine learning to devise robust algorithms, and includes automated leaf count obtained from 2D images without the need of depth (3D). Our affordable device (~200?) can be deployed in growth chambers or greenhouses to acquire optical 2D images of approximately up to 60 adult Arabidopsis rosettes concurrently. Data from the device are processed remotely on a workstation or via a cloud application (based on CyVerse). In this paper, we present a proof-of-concept validation experiment on top-view images of 24 Arabidopsis plants in a combination of genotypes that has not been previously compared. Their phenotypic analysis with respect to morphology, growth, color and leaf count has not been done previously comprehensively. We confirm findings of others on some of the extracted traits showing that we can phenotype at reduced cost. We also perform extensive validations with external measurements and with higher fidelity equipment and find no loss in statistical accuracy when we use the affordable setting we propose. Device setup instructions and analysis software are publicly available (http://phenotiki.com).

Get citation
@article{Minervini2017,
author = {Minervini, Massimo and Giuffrida, Mario Valerio and Perata, Pierdomenico and Tsaftaris, Sotirios A},
doi = {10.1111/tpj.13472},
issn = {1365-313X},
journal = {The Plant journal : for cell and molecular biology},
month = {jan},
pmid = {28066963},
title = {{Phenotiki: An open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/28066963},
year = {2017}
}
Theta-RBM: Unfactored Gated Restricted Boltzmann Machine for Rotation-Invariant Representations
Mario Valerio Giuffrida, Sotirios A. Tsaftaris

Learning invariant representations is a critical task in computer vision. In this paper, we propose the Theta-Restricted Boltzmann Machine (?-RBM in short), which builds upon the original RBM formulation and injects the notion of rotation-invariance during the learning procedure. In contrast to previous approaches, we do not transform the training set with all possible rotations. Instead, we rotate the gradient filters when they are computed during the Contrastive Divergence algorithm. We formulate our model as an unfactored gated Boltzmann machine, where another input layer is used to modulate the input visible layer to drive the optimisation procedure. Among our contributions is a mathematical proof that demonstrates that ?-RBM is able to learn rotation-invariant features according to a recently proposed invariance measure. Our method reaches an invariance score of ~90% on mnist-rot dataset, which is the highest result compared with the baseline methods and the current state of the art in transformation-invariant feature learning in RBM. Using an SVM classifier, we also showed that our network learns discriminative features as well, obtaining ~10% of testing error.

Get citation
@techreport{Giuffrida2016,
title = {{Theta-RBM: Unfactored Gated Restricted Boltzmann Machine for Rotation-Invariant Representations}},
author = {Giuffrida, Mario Valerio and Tsaftaris, Sotirios A.},
eprint = {1606.08805},
institution = {arXiv},
month = {jun},
pages = {9},
url = {http://arxiv.org/abs/1606.08805},
year = {2016}
}
Rotation-Invariant Restricted Boltzmann Machine using Shared Gradient Filters
Mario Valerio Giuffrida, Sotirios A. Tsaftaris

Finding suitable features has been an essential problem in computer vision. We focus on Restricted Boltzmann Machines (RBMs), which, despite their versatility, cannot accommodate transformations that may occur in the scene. As a result, several approaches have been proposed that consider a set of transformations, which are used to either augment the training set or transform the actual learned filters. In this paper, we propose the Explicit Rotation-Invariant Restricted Boltzmann Machine, which exploits prior information coming from the dominant orientation of images. Our model extends the standard RBM, byadding a suitable number of weight matrices, associated with each dominant gradient. We show that our approach is able to learn rotation-invariant features, comparing it with the classic formulation of RBM on the MNIST benchmark dataset. Overall, requiring less hidden units, our method learns compact features, which are robust to rotations.

Get citation
@Inbook{Giuffrida2016,
author="Giuffrida, Mario Valerio and Tsaftaris, Sotirios A.",
editor="Villa, Alessandro E.P. and Masulli, Paolo and Pons Rivero, Antonio Javier",
title="Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters",
bookTitle="Artificial Neural Networks and Machine Learning -- ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="480--488",
isbn="978-3-319-44781-0",
doi="10.1007/978-3-319-44781-0_57"
}
Whole Image Synthesis Using a Deep Encoder-Decoder Network
Vasileios Sevetlidis, Mario Valerio Giuffrida, Sotirios A. Tsaftaris

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 sequences). Most methods follow a patch-based approach, which is computationally inefficient during synthesis and requires some sort of ?fusion? to synthesize a whole image from patch-level results. In this paper, we present a whole image synthesis approach that relies on deep neural networks. Our architecture resembles those of encoder-decoder networks, which aims to synthesize a source MRI modality to an other target MRI modality. The proposed method is computationally fast, it doesn?t require extensive amounts of memory, and produces comparable results to recent patch-based approaches.

Get citation
@Inbook{Sevetlidis2016,
author="Sevetlidis, Vasileios and Giuffrida, Mario Valerio and Tsaftaris, Sotirios A.",
editor="Tsaftaris, Sotirios A. and Gooya, Ali and Frangi, Alejandro F. and Prince, Jerry L.",
title="Whole Image Synthesis Using a Deep Encoder-Decoder Network",
bookTitle="Simulation and Synthesis in Medical Imaging: First International Workshop, SASHIMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="127--137",
isbn="978-3-319-46630-9",
doi="10.1007/978-3-319-46630-9_13"
}
Learning to Count Leaves in Rosette Plants
Mario Valerio Giuffrida, Massimo Minervini, Sotirios A. Tsaftaris

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 learned in an unsupervised fashion to leaf counts using a supervised regression model. To take advantage of the circular and coplanar arrangement of leaves and also to introduce scale and rotation invariance, we learn features in a log-polar representation. Image patches extracted in this log-polar domain are provided to K-means, which builds a codebook in a unsupervised manner. Feature codes are obtained by projecting patches on the codebook using the triangle encoding, introducing both sparsity and specifically designed representation. A global, per-plant image descriptor is obtained by pooling local features in specific regions of the image. Finally, we provide the global descriptors to a support vector regression framework to estimate the number of leaves in a plant. We evaluate our method on datasets of the \textit{Leaf Counting Challenge} (LCC), containing images of Arabidopsis and tobacco plants. Experimental results show that on average we reduce absolute counting error by 40% w.r.t. the winner of the 2014 edition of the challenge -a counting via segmentation method. When compared to state-of-the-art density-based approaches to counting, on Arabidopsis image data ~75% less counting errors are observed. Our findings suggest that it is possible to treat leaf counting as a regression problem, requiring as input only the total leaf count per training image.

Get citation
@inproceedings{CVPP2015_1,
title={Learning to Count Leaves in Rosette Plants},
author={Mario Valerio Giuffrida and Massimo Minervini and Sotirios Tsaftaris},
year={2015},
month={September},
pages={1.1-1.13},
articleno={1},
numpages={13},
booktitle={Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP)},
publisher={BMVA Press},
editor={S. A. Tsaftaris, H. Scharr, and T. Pridmore},
doi={10.5244/C.29.CVPPP.1},
isbn={1-901725-55-3}
}
An interactive tool for semi-automated leaf annotation
Massimo Minervini, Mario Valerio Giuffrida, Sotirios A. Tsaftaris

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 information to be associated with genotypes and environmental conditions for identifying traits suitable for further development. Obtaining leaf level quantitative data is important towards understanding better this interaction. While certain efforts have been made to obtain such information in an automated fashion, further innovations are necessary. In this paper we present an annotation tool that can be used to semi-automatically segment leaves in images of rosette plants. This tool, which is designed to exist in a stand-alone fashion but also in cloud based environments, can be used to annotate data directly for the study of plant and leaf growth or to provide annotated datasets for learning-based approaches to extracting phenotypes from images. It relies on an interactive graph-based segmentation algorithm to propagate expert provided priors (in the form of pixels) to the rest of the image, using the random walk formulation to find a good per-leaf segmentation. To evaluate the tool we use standardized datasets available from the LSC and LCC 2015 challenges, achieving an average leaf segmentation accuracy of almost 97% using scribbles as annotations. The tool and source code are publicly available at http://www.phenotiki.com and as a GitHub repository at https://github.com/phenotiki/LeafAnnotationTool.

Get citation
@inproceedings{CVPP2015_6,
title={An interactive tool for semi-automated leaf annotation},
author={Massimo Minervini and Mario Valerio Giuffrida and Sotirios Tsaftaris},
year={2015},
month={September},
pages={6.1-6.13},
articleno={6},
numpages={13},
booktitle={Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP)},
publisher={BMVA Press},
editor={S. A. Tsaftaris, H. Scharr, and T. Pridmore},
doi={10.5244/C.29.CVPPP.6},
isbn={1-901725-55-3}
}
On Blind Source Camera Identification Algorithms
Giovanni Maria Farinella, Mario Valerio Giuffrida, Vincenzo Digiacomo, Sebastiano Battiato

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 easily tampered. This lead to the necessity of blind techniques to infer the acquisition device, by processing the content of a given image. Recent studies are concentrated on exploiting sensor pattern noise, or extracting a signature from the set of pictures. In this paper we compare two popular algorithms for the blind camera identification. The first approach extracts a fingerprint from a training set of images, by exploiting the camera sensor?s defects. The second one is based on image features extraction and it assumes that images can be affected by color processing and transformations operated by the camera prior to the storage. For the comparison we used two representative dataset of images acquired, using consumer and mobile cameras respectively. Considering both type of cameras this study is useful to understand whether the theories designed for classic consumer cameras maintain their performances on mobile domain.

Get citation
@Inbook{Farinella2015,
author="Farinella, G. M. and Giuffrida, M. V. and Digiacomo, V. and Battiato, S.",
editor="Battiato, Sebastiano and Blanc-Talon, Jacques and Gallo, Giovanni and Philips, Wilfried and Popescu, Dan and Scheunders, Paul",
title="On Blind Source Camera Identification",
bookTitle="Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="464--473",
isbn="978-3-319-25903-1",
doi="10.1007/978-3-319-25903-1_40"
}
Exploiting time-multiplexing structured light with picoprojectors
Mario Valerio Giuffrida, Giovanni Maria Farinella, Sebastiano Battiato, Mirko Guarnera

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 accomplish the task of 3D measurement through structured light. This technique allows to estimate the depth of the objects, by projecting specific light patterns on the measuring scene. The potentialities of the structured light are well-known in both scientific and industrial contexts. Our framework uses a picoprojector module provided by STMicroelectronics, driven by the designed software projecting time- multiplexing Gray code light patterns. The Gray code is an alternative method to represent binary numbers, ensuring that the hamming distance between two consecutive numbers is always one. Because of this property, this binary coding gives better results for depth estimation task. Many patterns are projected at different time instants, obtaining a dense coding for each pixel. This information is then used to compute the depth for each point in the image. In order to achieve better results, we integrate the depth estimation with the inverted Gray code patterns as well, to compensate projector-camera synchronization problems as well as noise in the scene. Even though our framework is designed for laser picoprojectors, it can be used with conventional image projectors and we present the results for this case too.

Get citation
@proceeding{doi:10.1117/12.2083031,
author = {Giuffrida, Mario Valerio and Farinella, Giovanni M. and Battiato, Sebastiano and Guarnera, Mirko},
title = {Exploiting time-multiplexing structured light with picoprojectors},
journal = {Proc. SPIE},
volume = {9393},
pages = {939304-939304-13},
year = {2015},
doi = {10.1117/12.2083031}
}