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19 results on '"Giovanni Fusco"'

1. Count Regression and Machine Learning Approach for Zero-Inflated Over-Dispersed Count Data. Application to Micro-Retail Distribution and Urban Form

Author

Alessandro Araldi, Giovanni Fusco, Alessandro Venerandi, Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Gervasi, Osvaldo, Murgante, Beniamino, Misra, Sanjay, Garau, Chiara, Blecic, Ivan, Taniar, David, Apduhan, Bernady O., Rocha, Ana Maria A.C., Tarantino, Eufemia, Torre, Carmelo Maria, and Karaca, Yeliz

Subjects
QA75, business.industry, Computer science, 0211 other engineering and technologies, Distribution (economics), 021107 urban & regional planning, Feature selection, 02 engineering and technology, [SHS.GEO]Humanities and Social Sciences/Geography, Machine learning, computer.software_genre, Regression, Zero (linguistics), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Selection (linguistics), 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Artificial intelligence, business, computer, ComputingMilieux_MISCELLANEOUS, Street network, Count data
Abstract

This paper investigates the relationship between urban form and the spatial distribution of micro-retail activities. In the last decades, several works demonstrated how configurational properties of the street network and morphological descriptors of the urban built environment are significantly related to store distribution. However, two main challenges still need to be addressed. On the one side, the combined effect of different urban form properties should be considered providing a holistic study of the urban form and its relationship to retail patterns. On the other, analytical approaches should consider the discrete, skewed and zero-inflated nature of the micro-retail distribution. To overcome these limitations, this work compares two sophisticated modelling procedure: Penalised Count Regression and Machine Learning approaches. While the former is specifically conceived to account for retail count distribution, the latter can capture non-linear behaviours in the data. The two modelling procedures are implemented on the same large dataset of street-based measures describing the urban form of the French Riviera. The outcomes of the two modelling approaches are compared in terms of prediction performance and selection frequencies of the most recurrent variables among the implemented models.

Published
2020
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2. Possibilistic Estimation of Distributions to Leverage Sparse Data in Machine Learning

Author

Célia da Costa Pereira, Alessandro Venerandi, Giovanni Fusco, Andrea G. B. Tettamanzi, David Emsellem, Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), K-City Labs - Kinaxia Group, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), IDEX 'UCA JEDI', AAP Partenariat 2016 (action 6.5), Marie-Jeanne Lesot, Susana M. Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager, UCA JEDI, ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015), ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Lesot, Marie-Jeanne, Vieira, Susana, Reformat, Marek Z., Carvalho, João Paulo, Wilbik, Anna, Bouchon-Meunier, Bernadette, and Yager, Ronald R.

Subjects
QA75, Urban form, Computer science, business.industry, Supervised learning, 0211 other engineering and technologies, 02 engineering and technology, Machine learning, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, Weakly Supervised Learning, QA273, 0202 electrical engineering, electronic engineering, information engineering, Leverage (statistics), 020201 artificial intelligence & image processing, Artificial intelligence, Possibility Theory, Market value, business, computer, 021101 geological & geomatics engineering, Possibility theory, Sparse matrix
Abstract

International audience; Prompted by an application in the area of human geography using machine learning to study housing market valuation based on the urban form, we propose a method based on possibility theory to deal with sparse data, which can be combined with any machine learning method to approach weakly supervised learning problems. More specifically, the solution we propose constructs a possibilistic loss function to account for an uncertain supervisory signal. Although the proposal is illustrated on a specific application, its basic principles are general. The proposed method is then empirically validated on real-world data.

Published
2020
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3. Indoor localization for visually impaired travelers using computer vision on a smartphone

Author

Giovanni Fusco and James M. Coughlan

Subjects
Computer science, business.industry, Visually impaired, Visual impairment, Information sign, Space (commercial competition), Article, Inertial measurement unit, Feature (computer vision), medicine, Global Positioning System, Computer vision, Artificial intelligence, medicine.symptom, business, Sensory cue
Abstract

Wayfinding is a major challenge for visually impaired travelers, who generally lack access to visual cues such as landmarks and informational signs that many travelers rely on for navigation. Indoor wayfinding is particularly challenging since the most commonly used source of location information for wayfinding, GPS, is inaccurate indoors. We describe a computer vision approach to indoor localization that runs as a real-time app on a conventional smartphone, which is intended to support a full-featured wayfinding app in the future that will include turn-by-turn directions. Our approach combines computer vision, existing informational signs such as Exit signs, inertial sensors and a 2D map to estimate and track the user’s location in the environment. An important feature of our approach is that it requires no new physical infrastructure. While our approach requires the user to either hold the smartphone or wear it (e.g., on a lanyard) with the camera facing forward while walking, it has the advantage of not forcing the user to aim the camera towards specific signs, which would be challenging for people with low or no vision. We demonstrate the feasibility of our approach with five blind travelers navigating an indoor space, with localization accuracy of roughly 1 meter once the localization algorithm has converged.

Published
2020
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4. A Machine Learning Approach to Study the Relationship between Features of the Urban Environment and Street Value

Author

Alessandro Venerandi, Giovanni Fusco, Andrea G. B. Tettamanzi, David Emsellem, Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), K-City Labs - Kinaxia Group, ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015), Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Université Nice Sophia Antipolis (... - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS)

Subjects
Multivariate statistics, Computer science, 0211 other engineering and technologies, 0507 social and economic geography, Urban environment, 02 engineering and technology, Machine learning, computer.software_genre, Street value, French Riviera, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], lcsh:Social Sciences, 11. Sustainability, Architecture, Set (psychology), Socioeconomic status, Ensemble method, business.industry, 05 social sciences, lcsh:Geography. Anthropology. Recreation, 021107 urban & regional planning, General Medicine, [SHS.GEO]Humanities and Social Sciences/Geography, Unit of analysis, lcsh:H, lcsh:G, Independence (mathematical logic), NA, Artificial intelligence, business, 050703 geography, computer, Value (mathematics)
Abstract

Understanding what aspects of the urban environment are associated with bettersocioeconomic/liveability outcomes is a long standing research topic. Several quantitative studieshave investigated such relationships. However, most of such works analysed single correlations, thusfailing to obtain a more complete picture of how the urban environment can contribute to explainthe observed phenomena. More recently, multivariate models have been suggested. However, theyuse a limited set of metrics, propose a coarse spatial unit of analysis, and assume linearity andindependence among regressors. In this paper, we propose a quantitative methodology to studythe relationship between a more comprehensive set of metrics of the urban environment and thevalorisation of street segments that handles non-linearity and possible interactions among variables,through the use of Machine Learning (ML). The proposed methodology was tested on the FrenchRiviera and outputs show a moderate predictive capacity (i.e., adjusted R2 = 0.75) and insightfulexplanations on the nuanced relationships between selected features of the urban environment andstreet values. These findings are clearly location specific, however, the methodology is replicable andcan thus inspire future research of this kind in different geographic contexts.

Published
2019
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6. Indoor Localization using Computer Vision and Visual-Inertial Odometry

Author

Giovanni Fusco and James M. Coughlan

Subjects
Physical infrastructure, Inertial frame of reference, business.industry, Computer science, 05 social sciences, Information sign, 02 engineering and technology, Article, Odometry, Dead reckoning, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, Computer vision, Artificial intelligence, Normal vision, business, Sensory cue, 050107 human factors, Sign (mathematics)
Abstract

Indoor wayfinding is a major challenge for people with visual impairments, who are often unable to see visual cues such as informational signs, land-marks and structural features that people with normal vision rely on for wayfinding. We describe a novel indoor localization approach to facilitate wayfinding that uses a smartphone to combine computer vision and a dead reckoning technique known as visual-inertial odometry (VIO). The approach uses sign recognition to estimate the user's location on the map whenever a known sign is recognized, and VIO to track the user's movements when no sign is visible. The ad-vantages of our approach are (a) that it runs on a standard smartphone and re-quires no new physical infrastructure, just a digital 2D map of the indoor environment that includes the locations of signs in it; and (b) it allows the user to walk freely without having to actively search for signs with the smartphone (which is challenging for people with severe visual impairments). We report a formative study with four blind users demonstrating the feasibility of the approach and suggesting areas for future improvement.

Published
2018

7. Structured multi-class feature selection with an application to face recognition

Author

Giovanni Fusco, Francesca Odone, Luca Zini, and Nicoletta Noceti

Subjects
business.industry, Local binary patterns, Pattern recognition, Feature selection, Machine learning, computer.software_genre, Facial recognition system, Set (abstract data type), Artificial Intelligence, Signal Processing, Benchmark (computing), Point (geometry), Computer Vision and Pattern Recognition, Artificial intelligence, business, Representation (mathematics), Focus (optics), computer, Software, Mathematics
Abstract

In this paper we address the problem of structured feature selection in a multi-class classification setting. Our goal is to select groups of features meaningful to all classes simultaneously, and to this purpose we derive a new formulation of Group LASSO - the MC-GrpLASSO - and a solution of the obtained functional based on proximal methods. We then apply the algorithm to a typical multi-class problem - face recognition. On this respect we focus on finding an effective and fast to compute (that is, sparse) representation of faces, detected in low quality videos of unconstrained environments. We start from a classical over-complete representation based on Local Binary Patterns (LBPs), descriptors endowed with a characteristic internal structure that can be preserved by selecting features in groups. We present an extensive experimental analysis on two benchmark datasets, MOBO and Choke Point, and on a more complex set of data acquired in-house over a large temporal span. We compare our results with state-of-the-art approaches and show the superiority of our method in terms of both performances and sparseness of the obtained solution.

Published
2015
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8. Towards a Sign-Based Indoor Navigation System for People with Visual Impairments

Author

Alejandro Rituerto, James M. Coughlan, and Giovanni Fusco

Subjects
business.industry, Computer science, 020208 electrical & electronic engineering, Navigation system, 020207 software engineering, 02 engineering and technology, Floor plan, Mobile robot navigation, Article, Variety (cybernetics), Task (project management), Navigational instrument, Formative assessment, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, Global Positioning System, Computer vision, Artificial intelligence, business
Abstract

Navigation is a challenging task for many travelers with visual impairments. While a variety of GPS-enabled tools can provide wayfinding assistance in outdoor settings, GPS provides no useful localization information indoors. A variety of indoor navigation tools are being developed, but most of them require potentially costly physical infrastructure to be installed and maintained, or else the creation of detailed visual models of the environment. We report development of a new smartphone-based navigation aid, which combines inertial sensing, computer vision and floor plan information to estimate the user's location with no additional physical infrastructure and requiring only the locations of signs relative to the floor plan. A formative study was conducted with three blind volunteer participants demonstrating the feasibility of the approach and highlighting the areas needing improvement.

Published
2017

9. A new urban segregation-growth coupled model using a belief-desire-intention possibilistic framework

Author

Célia da Costa Pereira, Giovanni Fusco, José Tiberio Hernández, Meili Vanegas-Hernandez, Andrea G. B. Tettamanzi, Diego A. Moreno, Michel Riveill, Universidad de los Andes [Bogota] (UNIANDES), Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), ECOS-Nord TOMSA (Transport Oriented Modeling for urban denSification Analysis) Project, Amit P. Sheth, Axel Ngonga, Yin Wang, Elizabeth Chang, Dominik Ślęzak, Bogdan Franczyk, Rainer Alt, Xiaohui Tao, Rainer Unland, Université Nice Sophia Antipolis (1965 - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)

Subjects
050402 sociology, Computer science, Belief desire intention, computer.software_genre, Track (rail transport), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Intelligent agent, Agent-Based Modeling, Sociology, 0504 sociology, Simple (abstract algebra), 0502 economics and business, Urban Segregation, Geosimulation, 050207 economics, Agent / discrete models, Possibility theory, Spatial contextual awareness, business.industry, 05 social sciences, [SHS.GEO]Humanities and Social Sciences/Geography, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Visualization, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], BDI Agent Model, Applied computing Economics, Artificial intelligence, Computing methodologies : Intelligent agents, business, computer
Abstract

International audience; We study the feasibility of using Belief, Desire and Intention agents for modeling the phenomena of urban growth and segregation. Uncertainty, typical of real world situations is modeled using possibility theory. We have also implemented a simple visualization tool whose aim is to track the changes in the model. Some preliminary experiments suggest that such an approach might allow a decision-maker to dynamically track the changes in the model. Besides, it is also possible to interact with the different steps of the simulation via the model. Our proposal makes it possible to simulate the interactions between cognitive agents in an economical environment while taking the spatial context into account.

Published
2017
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10. Uncertain logical gates in possibilistic networks: Theory and application to human geography

Author

Giovanni Fusco, Didier Dubois, Andrea G. B. Tettamanzi, Henri Prade, Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Centre National de la Recherche Scientifique (CNRS), Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), CNRS PEPS HuMaIn project 'GEO-INCERTITUDE', Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Institut National de la Recherche en Informatique et en Automatique - INRIA (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Côte d'Azur (FRANCE), and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)

Subjects
Knowledge engineering, human geography, 02 engineering and technology, possibility theory, Theoretical Computer Science, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, belief networks, expert knowledge, Artificial Intelligence, Computer Science::Logic in Computer Science, Human geography, 0202 electrical engineering, electronic engineering, information engineering, 030212 general & internal medicine, Uncertainty quantification, Mathematics, Possibility theory, Logique en informatique, Belief networks, business.industry, Applied Mathematics, Probabilistic logic, Bayesian network, Informatique et langage, [SHS.GEO]Humanities and Social Sciences/Geography, Intelligence artificielle, 16. Peace & justice, Apprentissage, Logic gate, Noisy gates, Expert knowledge, 020201 artificial intelligence & image processing, Artificial intelligence, business, Software, noisy gates
Abstract

Possibilistic networks offer a qualitative approach for modeling epistemic uncertainty. Their practical implementation requires the specification of conditional possibility tables, as in the case of Bayesian networks for probabilities. The elicitation of probability tables by experts is made much easier by means of noisy logical gates that enable multidimensional tables to be constructed from the knowledge of a few parameters. This paper presents the possibilistic counterparts of usual noisy connectives (and, or, max, min, ). Their interest and limitations are illustrated on an example taken from a human geography modeling problem. The difference of behavior between probabilistic and possibilistic connectives is discussed in detail. Results in this paper may be useful to bring possibilistic networks closer to applications. A definition of uncertain logical gates in possibilistic networks and their detailed expressions including the leaky cases.A comparison with noisy gates in probabilistic networks.An illustrative example of using such gates on an application to human geography.

Published
2017
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11. Uncertain Logical Gates in Possibilistic Networks. An Application to Human Geography

Author

Andrea G. B. Tettamanzi, Giovanni Fusco, Henri Prade, Didier Dubois, Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Centre National de la Recherche Scientifique (CNRS), Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), CNRS PEPS 'Geo-Incertitude', Christoph Beierle, Alex Dekhtyar, Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Aix-Marseille Université - AMU (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Nice Sophia Antipolis (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université d'Avignon et des Pays de Vaucluse (FRANCE), and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)

Subjects
Knowledge engineering, Possibilistic networks, Computer Science::Artificial Intelligence, Human Geography, Possibilistic Networks, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science::Logic in Computer Science, Human geography, Artificial Intelligence & Image Processing, Uncertainty quantification, Possibility Theory, Mathematics, Possibility theory, Logique en informatique, business.industry, Probabilistic logic, Bayesian network, Knowledge Engineering, Informatique et langage, Intelligence artificielle, 16. Peace & justice, Apprentissage, Logic gate, Artificial intelligence, business
Abstract

© Springer International Publishing Switzerland 2015. Possibilistic networks offer a qualitative approach for modeling epistemic uncertainty. Their practical implementation requires the specification of conditional possibility tables, as in the case of Bayesian networks for probabilities. This paper presents the possibilistic counterparts of the noisy probabilistic connectives (and, or, max, min,...). Their interest is illustrated on an example taken from a human geography modeling problem. The difference of behaviors in some cases of some possibilistic connectives, with respect to their probabilistic analogs, is discussed in details.

Published
2015
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12. Using Computer Vision to Access Appliance Displays

Author

Giovanni Fusco, Richard E. Ladner, Ender Tekin, and James M. Coughlan

Subjects
Prototype software, Multimedia, business.industry, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Glare (vision), USable, computer.software_genre, Article, Variety (cybernetics), Formative assessment, Human–computer interaction, Face (geometry), Smartphone app, Computer vision, Artificial intelligence, User interface, business, computer
Abstract

People who are blind or visually impaired face difficulties accessing a growing array of everyday appliances, needed to perform a variety of daily activities, because they are equipped with electronic displays. We are developing a "Display Reader" smartphone app, which uses computer vision to help a user acquire a usable image of a display, to address this problem. The current prototype analyzes video from the smartphone's camera, providing real-time feedback to guide the user until a satisfactory image is acquired, based on automatic estimates of image blur and glare. Formative studies were conducted with several blind and visually impaired participants, whose feedback is guiding the development of the user interface. The prototype software has been released as a Free and Open Source (FOSS) project.

Published
2014

13. Self-Localization at Street Intersections

Author

Giovanni Fusco, Huiying Shen, and James M. Coughlan

Subjects
Panorama, business.industry, Computer science, Article, Image stitching, Intersection, Inertial measurement unit, Metric (mathematics), Key (cryptography), Global Positioning System, Computer vision, Artificial intelligence, Scale (map), business
Abstract

There is growing interest among smartphone users in the ability to determine their precise location in their environment for a variety of applications related to way finding, travel and shopping. While GPS provides valuable self-localization estimates, its accuracy is limited to approximately 10 meters in most urban locations. This paper focuses on the self-localization needs of blind or visually impaired travelers, who are faced with the challenge of negotiating street intersections. These travelers need more precise self-localization to help them align themselves properly to crosswalks, signal lights and other features such as walk light pushbuttons. We demonstrate a novel computer vision-based localization approach that is tailored to the street intersection domain. Unlike most work on computer vision-based localization techniques, which typically assume the presence of detailed, high-quality 3D models of urban environments, our technique harnesses the availability of simple, ubiquitous satellite imagery (e.g., Google Maps) to create simple maps of each intersection. Not only does this technique scale naturally to the great majority of street intersections in urban areas, but it has the added advantage of incorporating the specific metric information that blind or visually impaired travelers need, namely, the locations of intersection features such as crosswalks. Key to our approach is the integration of IMU (inertial measurement unit) information with geometric information obtained from image panorama stitchings. Finally, we evaluate the localization performance of our algorithm on a dataset of intersection panoramas, demonstrating the feasibility of our approach.

Published
2014
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14. Determining a Blind Pedestrian’s Location and Orientation at Traffic Intersections

Author

Giovanni Fusco, Vidya Nariyambut murali, Huiying Shen, and James M. Coughlan

Subjects
business.industry, Computer science, Orientation (computer vision), Visual impairment, Poison control, Pedestrian, Article, Intersection, Global Positioning System, Schema crosswalk, medicine, Computer vision, Artificial intelligence, User interface, medicine.symptom, business, Simulation
Abstract

This paper describes recent progress on Crosswatch, a smartphone-based computer vision system developed by the authors for providing guidance to blind and visually impaired pedestrians at traffic intersections. One of Crosswatch's key capabilities is determining the user's location (with precision much better than what is obtainable by GPS) and orientation relative to the crosswalk markings in the intersection that he/she is currently standing at; this capability will be used to help him/her find important features in the intersection, such as walk lights, pushbuttons and crosswalks, and achieve proper alignment to these features. We report on two new contributions to Crosswatch: (a) experiments with a modified user interface, tested by blind volunteer participants, that makes it easier to acquire intersection images than with previous versions of Crosswatch; and (b) a demonstration of the system's ability to localize the user with precision better than what is obtainable by GPS, as well as an example of its ability to estimate the user's orientation.

Published
2014
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15. Structured multi-class feature selection for effective face recognition

Author

Luca Zini, Giovanni Fusco, Nicoletta Noceti, and Francesca Odone

Subjects
Lasso (statistics), Local binary patterns, Computer science, business.industry, Pipeline (computing), Benchmark (computing), Point (geometry), Feature selection, Pattern recognition, Artificial intelligence, Representation (mathematics), business, Facial recognition system
Abstract

This paper addresses the problem of real time face recognition in unconstrained environments from the analysis of low quality video frames. It focuses in particular on finding an effective and fast to compute (that is, sparse) representation of faces, starting from classical Local Binary Patterns (LBPs). The two contributions of the paper are a new formulation of Group LASSO for structured feature selection (MCGroup LASSO) to cope directly with multi-class settings, and a face recognition pipeline based on a representation derived from MC-GrpLASSO. We present an extensive experimental analysis on two benchmark datasets, MOBO and Choke Point, and on a more complex dataset acquired in-house over a large temporal span. We compare our results with state-of-the-art approaches and show the superiority of our method in terms of both performances and sparseness of the obtained solution.

Published
2013

17. Combining retrieval and classification for real-time face recognition

Author

Francesca Odone, Giovanni Fusco, and Nicoletta Noceti

Subjects
Matching (statistics), Contextual image classification, business.industry, Computer science, Wearable computer, Pattern recognition, Machine learning, computer.software_genre, Facial recognition system, Support vector machine, Face (geometry), Artificial intelligence, business, Image retrieval, computer, Real-time operating system
Abstract

In this paper we propose a real time face recognitionmethod that combines face matching and identity verificationmodules in a feedback loop, exploiting the temporalefficiency of matching and the performances of SVM classifiers. Our approach represents an ad-hoc solution for settingscharacterized by variable quantity, quality and distribution oflabeled data among the identities. We assess the procedure ontwo data sets of different complexities, showing the effectivenessof our solution. For its intrinsic peculiarities and its limitedcomputational cost the method finds application in real timesystems, and will be implemented on a wearable device forsupporting visually impaired people to localize known faces.

Published
2012

18. A Semi-automated Method for the Measurement of the Fetal Nuchal Translucency in Ultrasound Images

Author

Ezio Catanzariti, Giovanni Fusco, Salvatore Masecchia, Matteo Santoro, Roberto Prevete, Francesco Isgrò, Foggia, Pasquale and Sansone, Carlo and Vento, Mario, Catanzariti, Ezio, G., Fusco, Isgro', Francesco, S., Masecchia, Prevete, Roberto, and Santoro, Matteo

Subjects
Dynamic programming, First trimester, Highly skilled, Routine ultrasound, Nuchal translucency, business.industry, Region of interest, Computer science, Ultrasound, Computer vision, Artificial intelligence, business, Automated method
Abstract

Nowadays the measurement of the nuchal translucency thickness is being used as part of routine ultrasound scanning during the end of the first trimester of pregnancy, for the screening of chromosomal defects, as trisomy 21. Currently, the measurement is being performed manually by physicians. The measurement can take a long time for being accomplished, needs to be performed by highly skilled operators, and is prone to errors. In this paper we present an algorithm that automatically detects the border of the nuchal translucency, once a region of interest has been manually identified. The algorithm is based on the minimisation of a cost function, and the optimisation is performed using the dynamic programming paradigm. The method we present overcomes several of the drawbacks present in the state of the art algorithms.

Published
2009
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