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191 results on '"Tagliasacchi, M."'

5. Hash-based identification of sparse image tampering

Author

Tagliasacchi, M., Valenzise, G., and Tubaro, S.

Subjects
Data compression -- Analysis, Hashing (Computers) -- Usage, Image coding -- Analysis, Business, Computers, Electronics, Electronics and electrical industries
Published
2009

9. Poster Session Abstracts

Author

Bergamini, Gabriella, Peserico, Denise, Leal, T., Tagliasacchi, M., Ceri, S., Bianchi, F., Monti, Francesca, Sorio, E., Tridello, G., Laudanna, Eugenia, Sorio, Claudio, Assael, Baroukhmaurice, and Melotti, Paola Maria

Subjects
Pulmonary and Respiratory Medicine, Chemistry, Pseudomonas aeruginosa, Bicarbonate, Metabolism, medicine.disease_cause, Microbiology, chemistry.chemical_compound, CFTR, RATIOMETRIC MEASUREMENT, BETA ADRENERGIC/CHOLINERGIC SWEATING, HUMAN SWEAT GLANDS, RATIOMETRIC MEASUREMENT, Pediatrics, Perinatology and Child Health, medicine, Secretion, HUMAN SWEAT GLANDS, CFTR, Airway, BETA ADRENERGIC/CHOLINERGIC SWEATING
Published
2015

10. GreenEyes : Networked energy-aware visual analysis

Author

Baroffio, L., Cesana, M., Redondi, A., Tagliasacchi, M., Ascenso, J., Monteiro, P., Eriksson, Emil, Dan, G., Fodor, Viktoria, Baroffio, L., Cesana, M., Redondi, A., Tagliasacchi, M., Ascenso, J., Monteiro, P., Eriksson, Emil, Dan, G., and Fodor, Viktoria

Abstract

The GreenEyes project aims at developing a comprehensive set of new methodologies, practical algorithms and protocols, to empower wireless sensor networks with vision capabilities. The key tenet of this research is that most visual analysis tasks can be carried out based on a succinct representation of the image, which entails both global and local features, while it disregards the underlying pixel-level representation. Specifically, GreenEyes will pursue the following goals: i) energy-constrained extraction of visual features; ii) rate-efficiency modelling and coding of visual feature; iii) networking streams of visual features. This will have a significant impact on several scenarios including, e.g., smart cities and environmental monitoring., QC 20160203

Published
2015
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18. GreenEyes: Networked energy-aware visual analysis

Author

Baroffio, L., primary, Cesana, M., additional, Redondi, A., additional, Tagliasacchi, M., additional, Ascenso, J., additional, Monteiro, P., additional, Eriksson, E., additional, Dan, G., additional, and Fodor, V., additional

Published
2015
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19. Demo : Enabling image analysis tasks in visual sensor networks

Author

Baroffio, L., Canclini, A., Cesana, M., Redondi, A., Tagliasacchi, M., Dán, György, Eriksson, Emil, Fodor, Viktoria, Ascenso, J., Monteiro, P., Baroffio, L., Canclini, A., Cesana, M., Redondi, A., Tagliasacchi, M., Dán, György, Eriksson, Emil, Fodor, Viktoria, Ascenso, J., and Monteiro, P.

Abstract

This demo showcases some of the results obtained by the GreenEyes project, whose main objective is to enable visual analysis on resource-constrained multimedia sensor networks. The demo features a multi-hop visual sensor network operated by BeagleBones Linux computers with IEEE 802.15.4 communication capabilities, and capable of recognizing and tracking objects according to two different visual paradigms. In the traditional compress-then-analyze (CTA) paradigm, JPEG compressed images are transmitted through the network from a camera node to a central controller, where the analysis takes place. In the alternative analyze-then-compress (ATC) paradigm, the camera node extracts and compresses local binary visual features from the acquired images (either locally or in a distributed fashion) and transmits them to the central controller, where they are used to perform object recognition/tracking. We show that, in a bandwidth constrained scenario, the latter paradigm allows to reach better results in terms of application frame rates, still ensuring excellent analysis performance., QC 20141218

Published
2014
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20. Building the social graph of the history of European integration: A pipeline for humanist-machine interaction in the digital humanities

Author

Seventh Framework Programme, FP7: 287704 [sponsor], Living Analytics Reserch Centre (LARC);Rakuten;RECRUIT [sponsor], Wieneke, Lars, Düring, M., Silaume, G., Lallemand, Carine, Croce, V., Lazzarro, M., Nucci, F., Pasini, C., Fraternali, P., Tagliasacchi, M., Melenhorst, M., Novak, J., Micheel, I., Harloff, E., Garcia Moron, J., Seventh Framework Programme, FP7: 287704 [sponsor], Living Analytics Reserch Centre (LARC);Rakuten;RECRUIT [sponsor], Wieneke, Lars, Düring, M., Silaume, G., Lallemand, Carine, Croce, V., Lazzarro, M., Nucci, F., Pasini, C., Fraternali, P., Tagliasacchi, M., Melenhorst, M., Novak, J., Micheel, I., Harloff, E., and Garcia Moron, J.

Abstract

The breadth and scale of multimedia archives provides a tremendous potential for historical research that hasn't been fully tapped up to know. In this paper we want to discuss the approach taken by the History of Europe application, a demonstrator for the integration of human and machine computation that combines the power of face recognition technology with two distinctively different crowd-sourcing approaches to compute co-occurrences of persons in historical image sets. These co-occurrences are turned into a social graph that connects persons with each other and positions them, through information about the date and location of recording, in time and space. The resulting visualization of the graph as well as analytical tools can help historians to find new impulses for research and to un-earth previously unknown relationships. As such the integration of human expertise and machine computation enables a new class of applications for the exploration of multimedia archives with significant potential for the digital humanities. © 2014 Springer-Verlag.

Published
2014

29. Demo

Author

Baroffio, L., primary, Canclini, A., additional, Cesana, M., additional, Redondi, A., additional, Tagliasacchi, M., additional, Dán, G., additional, Eriksson, E., additional, Fodor, V., additional, Ascenso, J., additional, and Monteiro, P., additional

Published
2014
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30. Enabling visual analysis in wireless sensor networks

Author

Baroffio, L., primary, Canclini, A., additional, Redondi, M. Cesana A., additional, Tagliasacchi, M., additional, Dan, G., additional, Eriksson, E., additional, Fodor, V., additional, Ascenso, J., additional, and Monteiro, P., additional

Published
2014
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35. Semantically improved genome-wide prediction of Gene Ontology annotations

Author

Masseroli, M, Tagliasacchi, M, Chicco, D, Masseroli, M, Tagliasacchi, M, and Chicco, D

Abstract

Genomic annotations describing structural and functional features of genes and gene products through controlled terminologies and ontologies are extremely valuable, especially for computational analyses aimed at inferring new biomedical knowledge, which rely on available annotations. Yet, they are incomplete, especially for recently studied genomes, and only some of available annotations represent highly reliable human curated information. In order to help and speedup the time-consuming curation process and improve available annotations, computational methods able to provide prioritized lists of predicted annotations are paramount. Starting from a previous work on automatic prediction of Gene Ontology annotations based on singular value decomposition (SVD) of gene-to-term annotation matrix, here we propose a novel prediction algorithm that incorporates gene clustering based on gene functional similarity computed on Gene Ontology annotations. We tested both prediction methods performing k-fold cross-validation on two organism genomes, Saccharomyces cerevisiae (SGD) and Drosophila melanogaster (FlyBase). Results demonstrate effectiveness of our approach.

Published
2011

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