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Emanuele FRONTONI

Pubblicazioni

Emanuele FRONTONI

 

254 pubblicazioni classificate nel seguente modo:

Nr. doc. Classificazioni
130 4 Contributo in Atti di Convegno (Proceeding)
76 1 Contributo su Rivista
39 2 Contributo in Volume
4 6 Brevetti
3 3 Libro
1 5 Altro
1 8 Tesi di dottorato
Anno Risorsa
2021 ScoolAR: an educational platform to improve students’ learning through Virtual Reality
IEEE ACCESS
Autore/i: Puggioni, Mariapaola; Frontoni, Emanuele; Paolanti, Marina; Pierdicca, Roberto
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/287819 Collegamento a IRIS

2020 Preterm infants' pose estimation with spatio-temporal features
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Autore/i: Moccia, Sara; Migliorelli, Lucia; Carnielli, Virgilio; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: Preterm infants' limb monitoring in neonatal intensive care units (NICUs) is of primary importance for assessing infants' health status and motor/cognitive development. Herein, we propose a new approach to preterm-infants' limb pose estimation that features spatio-temporal information to detect and track limb joint position from depth videos with high reliability.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272708 Collegamento a IRIS

2020 Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach
ARTIFICIAL INTELLIGENCE IN MEDICINE
Autore/i: Bernardini, M.; Morettini, M.; Romeo, L.; Frontoni, E.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Early prediction of target patients at high risk of developing Type 2 diabetes (T2D) plays a significant role in preventing the onset of overt disease and its associated comorbidities. Although fundamental in early phases of T2D natural history, insulin resistance is not usually quantified by General Practitioners (GPs). Triglyceride-glucose (TyG) index has been proven useful in clinical studies for quantifying insulin resistance and for the early identification of individuals at T2D risk but still not applied by GPs for diagnostic purposes. The aim of this study is to propose a multiple instance learning boosting algorithm (MIL-Boost) for creating a predictive model capable of early prediction of worsening insulin resistance (low vs high T2D risk) in terms of TyG index. The MIL-Boost is applied to past electronic health record (EHR) patients’ information stored by a single GP. The proposed MIL-Boost algorithm proved to be effective in dealing with this task, by performing better than the other state-of-the-art ML competitors (Recall from 0.70 and up to 0.83). The proposed MIL-based approach is able to extract hidden patterns from past EHR temporal data, even not directly exploiting triglycerides and glucose measurements. The major advantages of our method can be found in its ability to model the temporal evolution of longitudinal EHR data while dealing with small sample size and variability in the observations (e.g., a small variable number of prescriptions for non-hospitalized patients). The proposed algorithm may represent the main core of a clinical decision support system.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/280099 Collegamento a IRIS

2020 Measurement of users’ well-being through domotic sensors and machine learning algorithms
IEEE SENSORS JOURNAL
Autore/i: Casaccia, Sara; Romeo, Luca; Calvaresi, Andrea; Morresi, Nicole; Monteriu', Andrea; Frontoni, Emanuele; Scalise, Lorenzo; Revel, Gian Marco
Classificazione: 1 Contributo su Rivista
Abstract: This paper proposes a specific domotic sensor network to measure the well-being of elderly people in private home environments through Machine Learning (ML) algorithms trained with daily surveys. The tests have been conducted in 5 apartments lived by 8 older people where the non-obtrusive sensor network is installed. Two ML algorithms are compared, Random Forest (RF) and Regression Tree (RT), such that to verify whether the users’ well-being is encoded in behavioural patterns obtained from the domotic data. These data are used to measure users’ well-being and compared with three reference indices obtained through a daily survey: a physical (Phy), a mental (Mind) and a general health index (Avg). The extracted indices from the daily survey are used to train ML algorithms in the estimation of user’s well-being for users that live alone (single-resident) or with others (multi-resident). Single-house and multi-house procedures are tested, both to extract a user-specific behaviour, and assess whether the model is able to generalise across different users and environments. Results show that the RF algorithm provides better performance than the RT algorithm in predicting the level of well-being with a Mean Absolute Error in the multi-house procedure of 32%, 13% and 17% for the Avg, Mind and Phy indices, respectively.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/277376 Collegamento a IRIS

2020 HATS project for lean and smart global logistic: A shipping company case study
MANUFACTURING LETTERS
Autore/i: Frontoni, Emanuele; Paolanti, Marina; Rosetti, Roberto; Anabela, Alves
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/282886 Collegamento a IRIS

2020 A Novel Spatio-Temporal Multi-Task Approach for the Prediction of Diabetes-Related Complication: a Cardiopathy Case of Study
Proceedings of the Twenty-Ninth International Joint Conference onArtificial Intelligence, {IJCAI-20}
Autore/i: Romeo, Luca; Armentano, Giuseppe; Nicolucci, Antonio; Vespasiani, Marco; Vespasiani, Giacomo; Frontoni, Emanuele
Editore: International Joint Conferences on Artificial Intelligence Organization
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/283219 Collegamento a IRIS

2020 Digital interaction with 3D archaeological artefacts: evaluating user’s behaviours at different representation scales
DIGITAL APPLICATIONS IN ARCHAEOLOGY AND CULTURAL HERITAGE
Autore/i: Quattrini, Ramona; Pierdicca, Roberto; Paolanti, Marina; Clini, Paolo; Nespeca, Romina; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284037 Collegamento a IRIS

2020 Deep understanding of shopper behaviours and interactions using RGB-D vision
MACHINE VISION AND APPLICATIONS
Autore/i: Paolanti, Marina; Pietrini, Rocco; Mancini, Adriano; Frontoni, Emanuele; Zingaretti, Primo
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284038 Collegamento a IRIS

2020 A regression framework to head-circumference delineation from US fetal images
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Autore/i: Fiorentino, Maria Chiara; Moccia, Sara; Capparuccini, Morris; Giamberini, Sara; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284009 Collegamento a IRIS

2020 A Content Creation Tool for AR/VR Applications in Education: The ScoolAR Framework
A Content Creation Tool for AR/VR Applications in Education: The ScoolAR Framework.
Autore/i: Puggioni, Mariapaola; Frontoni, Emanuele; Paolanti, Marina; Pierdicca, Roberto; Malinverni, Eva Savina; Sasso, Michele
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284035 Collegamento a IRIS

2020 Multidisciplinary Pattern Recognition applications: A review
COMPUTER SCIENCE REVIEW
Autore/i: Paolanti, Marina; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/282691 Collegamento a IRIS

2020 Supervised CNN Strategies for Optical Image Segmentation and Classification in Interventional Medicine
Deep Learners and Deep Learner Descriptors for Medical Applications
Autore/i: Moccia, Sara; Romeo, Luca; Migliorelli, Lucia; Frontoni, Emanuele; Zingaretti, Primo
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/279085 Collegamento a IRIS

2020 Evaluating Augmented and Virtual Reality in Education Through a User-Centered Comparative Study
Virtual and augmented reality in education, art, and museums
Autore/i: Pierdicca, Roberto; Frontoni, Emanuele; Puggioni, Maria Paola; Malinverni, Eva Savina; Paolanti, Marina
Classificazione: 2 Contributo in Volume
Abstract: Augmented and virtual reality proved to be valuable solutions to convey contents in a more appealing and interac- tive way. Given the improvement of mobile and smart devices in terms of both usability and computational power, contents can be easily conveyed with a realism level never reached in the past. Despite the tremendous number of researches related with the presentation of new fascinating applications of ancient goods and artifacts augmenta- tion, few papers are focusing on the real effect these tools have on learning. Within the framework of SmartMarca project, this chapter focuses on assessing the potential of AR/VR applications specifically designed for cultural heritage. Tests have been conducted on classrooms of teenagers to whom different learning approaches served as an evaluation method about the effectiveness of using these technologies for the education process. The chapter argues on the necessity of developing new tools to enable users to become producers of contents of AR/VR experiences.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/276434 Collegamento a IRIS

2020 Transfer learning for informative-frame selection in laryngoscopic videos through learned features
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
Autore/i: Patrini, Ilaria; Ruperti, Michela; Moccia, Sara; Mattos, Leonardo S.; Frontoni, Emanuele; De Momi, Elena
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/275500 Collegamento a IRIS

2020 Evaluating the autonomy of children with autism spectrum disorder in washing hands: a deep-learning approach
2020 IEEE Symposium on Computers and Communications (ISCC)
Autore/i: Berardini, Daniele; Migliorelli, Lucia; Moccia, Sara; Naldini, Marcello; Angelis, Gioia De; Frontoni, Emanuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284584 Collegamento a IRIS

2020 Augmented Reality Smart Glasses in the Workplace: Safety and Security in the Fourth Industrial Revolution Era
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Pierdicca, R.; Prist, M.; Monteriu, A.; Frontoni, E.; Ciarapica, F.; Bevilacqua, M.; Mazzuto, G.
Editore: Springer Science and Business Media Deutschland GmbH
Classificazione: 2 Contributo in Volume
Abstract: Industry 4.0 is reinventing the way in which production is performed. Based on its eight pillars, I4.0 environments are adopting digital solutions in order to make production smart. One of these is the concept of augmented operator, which can act with the aid of digital tools to facilitate daily work. Augmented Reality can represent the turnkey. In this light, the aim of this research is to present a case study of a “security and safety” application through the use of AR smart glasses, tested in a real scenario. For our experiments, Vuzix Blade smart glasses have been tested in combination with a cloud-based architecture connected with an oil-extractor plant. The goal is to develop an AR application that allows to assist the operator during the working process. In particular, it acts as a guide system for the operator who wears glasses, provides remote support (remote operator) and, from a security point of view, sends real-time alerts in dangerous situations. The application has been validated after a number of practical tests carried out by specialised technicians who normally perform the work.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284960 Collegamento a IRIS

2020 Faster R-CNN approach for detection and quantification of DNA damage in comet assay images
COMPUTERS IN BIOLOGY AND MEDICINE
Autore/i: Rosati, Riccardo; Romeo, Luca; Silvestri, Sonia; Marcheggiani, Fabio; Tiano, Luca; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284712 Collegamento a IRIS

2020 ICT driven platform for high-quality virtual contents creation and sharing with e-Tourism purposes. The interreg IT-HR REMEMBER project
CEUR Workshop Proceedings
Autore/i: Clini, P.; Frontoni, E.; Nespeca, R.; Quattrini, R.; Pierdicca, R.
Editore: CEUR-WS
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper describes REMEMBER, an EU project which aims at establishing a network for joint valorization of 8 ports in Italy and Croatia, trying to shift the paradigm of touristic flows toward a sustainable tourism. The key point is an innovative ICT architecture, modular and scalable, to share information at different system levels of detail and fruition, with an interoperable and multi-channel approach. Given its flexibility, contents can be conveniently displayed in different ways: Web portals, fixed installations, mobile devices etc. This infrastructure enables a great number of Digital Experiences (DEs) that can be exploited at both global and local scale. Since the project is on going, the paper presents a first overview of the instantiate methodology, as well as briefly introduces the DEs that are currently designed and, finnaly, reports a prospective outlook related to the post-pandemic scenario.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/286110 Collegamento a IRIS

2020 Functional evaluation of triceps surae during heel rise test: from EMG frequency analysis to machine learning approach
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
Autore/i: Ferracuti, Francesco; Fioretti, Sandro; Frontoni, Emanuele; Iarlori, Sabrina; Mengarelli, Alessandro; Riccio, Michele; Romeo, Luca; Verdini, Federica
Classificazione: 1 Contributo su Rivista
Abstract: Soleus muscle flap as coverage tissue is a possible surgical solution adopted to cover the wounds due to open fractures. Despite this procedure presents many clinical advantages, relatively poor information is available about the loss of functionality of triceps surae of the treated leg. In this study, a group of patients who underwent a soleus muscle flap surgical procedure has been analyzed through the heel rise test (HRT), in order to explore the triceps surae residual functionalities. A frequency band analysis was performed in order to assess whether the residual heads of triceps surae exhibit different characteristics with respect to both the non-treated lower limb and an age-matched control group. Then, an in-depth analysis based on a machine learning approach was proposed for discriminating between groups by generalizing across new unseen subjects. Experimental results showed the reliability of the proposed analyses for discriminating between-group at a specific time epoch and the high interpretability of the proposed machine learning algorithm allowed the temporal localization of the most discriminative frequency bands. Findings of this study highlighted that significant differences can be recognized in the myoelectric spectral characteristics between the treated and contralateral leg in patients who underwent soleus flap surgery. These experimental results may support the clinical decision-making for assessing triceps surae performance and for supporting the choice of treatment in plastic and reconstructive surgery.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/286181 Collegamento a IRIS

2020 The babyPose dataset
DATA IN BRIEF
Autore/i: Migliorelli, L.; Moccia, S.; Pietrini, R.; Carnielli, V. P.; Frontoni, E.
Classificazione: 1 Contributo su Rivista
Abstract: The database here described contains data relevant to preterm infants' movement acquired in neonatal intensive care units (NICUs). The data consists of 16 depth videos recorded during the actual clinical practice. Each video consists of 1000 frames (i.e., 100s). The dataset was acquired at the NICU of the Salesi Hospital, Ancona (Italy). Each frame was annotated with the limb-joint location. Twelve joints were annotated, i.e., left and right shoul- der, elbow, wrist, hip, knee and ankle. The database is freely accessible at http://doi.org/10.5281/zenodo.3891404. This dataset represents a unique resource for artificial intelligence researchers that want to develop algorithms to provide healthcare professionals working in NICUs with decision support. Hence, the babyPose dataset is the first annotated dataset of depth images relevant to preterm infants' movement analysis.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284264 Collegamento a IRIS

2020 AI4AR: An AI-Based Mobile Application for the Automatic Generation of AR Contents
AI4AR: An AI-Based Mobile Application for the Automatic Generation of AR Contents
Autore/i: Pierdicca, Roberto; Paolanti, Marina; Frontoni, Emanuele; Baraldi, Lorenzo
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284033 Collegamento a IRIS

2020 Augmented Reality Smart Glasses in the Workplace: Safety and Security in the Fourth Industrial Revolution Era
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Pierdicca, R.; Prist, M.; Monteriu, A.; Frontoni, E.; Ciarapica, F.; Bevilacqua, M.; Mazzuto, G.
Editore: Springer Science and Business Media Deutschland GmbH
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Industry 4.0 is reinventing the way in which production is performed. Based on its eight pillars, I4.0 environments are adopting digital solutions in order to make production smart. One of these is the concept of augmented operator, which can act with the aid of digital tools to facilitate daily work. Augmented Reality can represent the turnkey. In this light, the aim of this research is to present a case study of a “security and safety” application through the use of AR smart glasses, tested in a real scenario. For our experiments, Vuzix Blade smart glasses have been tested in combination with a cloud-based architecture connected with an oil-extractor plant. The goal is to develop an AR application that allows to assist the operator during the working process. In particular, it acts as a guide system for the operator who wears glasses, provides remote support (remote operator) and, from a security point of view, sends real-time alerts in dangerous situations. The application has been validated after a number of practical tests carried out by specialised technicians who normally perform the work.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284040 Collegamento a IRIS

2020 GRAPH CNN with RADIUS DISTANCE for SEMANTIC SEGMENTATION of HISTORICAL BUILDINGS TLS POINT CLOUDS
INTERNATIONAL ARCHIVES OF THE PHOTOGRAMMETRY, REMOTE SENSING AND SPATIAL INFORMATION SCIENCES
Autore/i: Morbidoni, C.; Pierdicca, R.; Quattrini, R.; Frontoni, E.
Classificazione: 1 Contributo su Rivista
Abstract: Point clouds obtained via Terrestrial Laser Scanning (TLS) surveys of historical buildings are generally transformed into semantically structured 3D models with manual and time-consuming workflows. The importance of automatizing this process is widely recognized within the research community. Recently, deep neural architectures have been applied for semantic segmentation of point clouds, but few studies have evaluated them in the Cultural Heritage domain, where complex shapes and mouldings make this task challenging. In this paper, we describe our experiments with the DGCNN architecture to semantically segment historical buildings point clouds, acquired with TLS. We propose a variation of the original approach where a radius distance based technique is used instead of K-Nearest Neighbors (KNN) to represent the neighborhood of points. We show that our approach provides better results by evaluating it on two real TLS point clouds, representing two Italian historical buildings: the Ducal Palace in Urbino and the Palazzo Ferretti in Ancona.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284969 Collegamento a IRIS

2020 Point cloud semantic segmentation using a deep learning framework for cultural heritage
REMOTE SENSING
Autore/i: Pierdicca, R.; Paolanti, M.; Matrone, F.; Martini, M.; Morbidoni, C.; Malinverni, E. S.; Frontoni, E.; Lingua, A. M.
Classificazione: 1 Contributo su Rivista
Abstract: In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/280903 Collegamento a IRIS

2020 A visual attentive model for discovering patterns in eye-tracking data—A proposal in cultural heritage
SENSORS
Autore/i: Pierdicca, R.; Paolanti, M.; Quattrini, R.; Mameli, M.; Frontoni, E.
Classificazione: 1 Contributo su Rivista
Abstract: In the Cultural Heritage (CH) context, art galleries and museums employ technology devices to enhance and personalise the museum visit experience. However, the most challenging aspect is to determine what the visitor is interested in. In this work, a novel Visual Attentive Model (VAM) has been proposed that is learned from eye tracking data. In particular, eye-tracking data of adults and children observing five paintings with similar characteristics have been collected. The images are selected by CH experts and are-the three “Ideal Cities” (Urbino, Baltimore and Berlin), the Inlaid chest in the National Gallery of Marche and Wooden panel in the “Studiolo del Duca” with Marche view. These pictures have been recognized by experts as having analogous features thus providing coherent visual stimuli. Our proposed method combines a new coordinates representation from eye sequences by using Geometric Algebra with a deep learning model for automated recognition (to identify, differentiate, or authenticate individuals) of people by the attention focus of distinctive eye movement patterns. The experiments were conducted by comparing five Deep Convolutional Neural Networks (DCNNs), yield high accuracy (more than 80%), demonstrating the effectiveness and suitability of the proposed approach in identifying adults and children as museums’ visitors.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/280898 Collegamento a IRIS

2020 Heartbeat detection by Laser Doppler Vibrometry and Machine Learning
SENSORS
Autore/i: Antognoli, Luca; Moccia, Sara; Migliorelli, Lucia; Casaccia, Sara; Scalise, Lorenzo; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/283881 Collegamento a IRIS

2020 SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0
INFORMATION
Autore/i: Calabrese, Matteo; Cimmino, Martin; Fiume, Francesca; Manfrin, Martina; Romeo, Luca; Ceccacci, Silvia; Paolanti, Marina; Toscano, Giuseppe; Ciandrini, Giovanni; Carrotta, Alberto; Mengoni, Maura; Frontoni, Emanuele; Kapetis, Dimos
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/281899 Collegamento a IRIS

2020 A Decision Support System for Diabetes Chronic Care Models based on General Practitioner engagement and EHR data sharing
IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE
Autore/i: Frontoni, Emanuele; Romeo, Luca; Bernardini, Michele; Moccia, Sara; Migliorelli, Lucia; Paolanti, Marina; Ferri, Alessandro; Misericordia, Paolo; Mancini, Adriano; Zingaretti, Primo
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284443 Collegamento a IRIS

2020 Machine Learning in Capital Markets: Decision Support System for Outcome Analysis
IEEE ACCESS
Autore/i: Rosati, R.; Romeo, L.; Goday, C. A.; Menga, T.; Frontoni, E.
Classificazione: 1 Contributo su Rivista
Abstract: Decision support systems using Artificial Intelligence in the context of financial services include different application ranging from investment advice to financial trading. The analysis of order flow provides many challenges that can be addressed by Machine Learning (ML) techniques in order to determine an optimal dynamic trading strategy. The first step in this direction is represented by the outcome analysis of order flow: the model should identify strong predictors that determine a positive/negative outcome. The aim of this work is the proposal of a closed-loop ML approach based on decision tree (DT) model to perform outcome analysis on financial trading data. The overall approach is integrated in a Decision Support System for Outcome Analysis (DSS-OA). Taking into account the model complexity, the DT algorithm enables to generate explanations that allow the user to understand (i) how this outcome is reached (decision rules) and (ii) the most discriminative outcome predictors (feature importance). The closed-loop approach allows the users to interact directly with the proposed DSS-OA by retraining the algorithm with the goal to a finer-grained outcome analysis. The experimental results and comparisons demonstrated high-interpretability and predictive performance of the proposed DSS-OA by providing a valid and fast system for outcome analysis on financial trading data. Moreover, the Proof of Concept evaluation demonstrated the impact of the proposed DSS-OA in the outcome analysis scenario.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/282792 Collegamento a IRIS

2020 Open-world person re-identification with RGBD camera in top-view configuration for retail applications
IEEE ACCESS
Autore/i: Martini, M.; Paolanti, M.; Frontoni, E.
Classificazione: 1 Contributo su Rivista
Abstract: Person re-identification (re-ID) is currently a notably topic in the computer vision and pattern recognition communities. However, most of the existing works on re-ID have been designed for closed world scenarios, rather than more realistic open world scenarios, limiting the practical application of these re-ID techniques. In a common real-world application, a watch-list of known people is given as the gallery/target set for searching through a large volume of videos where the people on the watch-list are likely to return. This aspect is fundamental in retail for understanding how customers schedule their shopping. The identification of regular and occasional customers allows to define temporal purchasing profiles, which can put in correlation the customers' temporal habits with other information such as the amount of expenditure and number of purchased items. This paper presents the first attempt to solve a more realistic re-ID setting, designed to face these important issues called Top-View Open-World (TVOW) person re-id. The approach is based on a pretrained Deep Convolutional neural Network (DCNN), finetuned on a dataset acquired by using a top-view configuration. A special loss function called triplet loss was used to train the network. The triplet loss optimizes the embedding space such that data points with the same identity are closer to each other than those with different identities. The TVOW is evaluated on the TVPR2 dataset for people re-ID that is publicly available. The experimental results show that the proposed methods significantly outperform all competitive state-of-the-art methods, bringing to different and significative insights for implicit and extensive shopper behaviour analysis for marketing applications.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/281504 Collegamento a IRIS

2019 Sharing health data among general practitioners: The Nu.Sa. project
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
Autore/i: Frontoni, Emanuele; Mancini, Adriano; Baldi, Marco; Paolanti, Marina; Moccia, Sara; Zingaretti, Primo; Landro, Vincenzo; Misericordia, Paolo
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266851 Collegamento a IRIS

2019 Learning-Based Screening of Endothelial Dysfunction From Photoplethysmographic Signals
ELECTRONICS
Autore/i: Calamanti, Chiara; Moccia, Sara; Migliorelli, Lucia; Paolanti, Marina; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/264089 Collegamento a IRIS

2019 TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records
COMPUTERS IN BIOLOGY AND MEDICINE
Autore/i: Bernardini, M.; Morettini, M.; Romeo, L.; Frontoni, E.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Insulin resistance is an early-stage deterioration of Type 2 diabetes. Identification and quantification of insulin resistance requires specific blood tests; however, the triglyceride-glucose (TyG) index can provide a surrogate assessment from routine Electronic Health Record (EHR) data. Since insulin resistance is a multi-factorial condition, to improve its characterisation, this study aims to discover non-trivial clinical factors in EHR data to determine where the insulin-resistance condition is encoded.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/269259 Collegamento a IRIS

2019 Robotic retail surveying by deep learning visual and textual data
ROBOTICS AND AUTONOMOUS SYSTEMS
Autore/i: Paolanti, M.; Romeo, L.; Martini, M.; Mancini, A.; Frontoni, E.; Zingaretti, P.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271293 Collegamento a IRIS

2019 Challenges of multi/hyper spectral images in precision agriculture applications
IOP Conference Series: Earth and Environmental Science
Autore/i: Mancini, A.; Frontoni, E.; Zingaretti, P.
Editore: Institute of Physics Publishing
Luogo di pubblicazione: DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271291 Collegamento a IRIS

2019 Satellite and UAV data for precision agriculture applications
2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019
Autore/i: Mancini, A.; Frontoni, E.; Zingaretti, P.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271292 Collegamento a IRIS

2019 An IoT edge-fog-cloud architecture for vision based planogram integrity
ACM International Conference Proceeding Series
Autore/i: Pietrini, R.; Placidi, V.; Manco, D.; Frontoni, E.; Paolanti, M.; Zingaretti, P.
Editore: Association for Computing Machinery
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271295 Collegamento a IRIS

2019 CNN Implementation for Semantic Heads Segmentation Using Top-View Depth Data in Crowded Environment
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Pietrini, R.; Liciotti, D.; Paolanti, M.; Frontoni, E.; Zingaretti, P.
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271289 Collegamento a IRIS

2019 A sequential deep learning application for recognising human activities in smart homes
NEUROCOMPUTING
Autore/i: Liciotti, D.; Bernardini, M.; Romeo, L.; Frontoni, E.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271339 Collegamento a IRIS

2019 An innovative design support system for industry 4.0 based on machine learning approaches
Proceedings of the 2018 5th International Symposium on Environment-Friendly Energies and Applications, EFEA 2018
Autore/i: Romeo, L.; Paolanti, M.; Bocchini, G.; Loncarski, J.; Frontoni, E.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271366 Collegamento a IRIS

2019 How to Extract Useful Information about the Decay of Bass Relieves in Archaeological Area
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Autore/i: Malinverni, E. S.; Pierdicca, R.; Di Stefano, F.; Sturari, M.; Mameli, M.; Frontoni, E.; Orazi, R.; Colosi, F.
Editore: Copernicus GmbH
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271361 Collegamento a IRIS

2019 MyDi application: Towards automatic activity annotation of young patients with Type 1 diabetes
2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT)
Autore/i: Migliorelli, Lucia; Moccia, Sara; Avellino, Ismaela; Fiorentino, Maria Chiara; Frontoni, Emanuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272360 Collegamento a IRIS

2019 Programmazione ad oggetti per l'ingegneria informatica
Create
Autore/i: Frontoni, Emanuele; Mancini, Adriano
Editore: McGraw-Hill Education
Classificazione: 3 Libro
Abstract: Il Custom Publishing di McGraw-Hill Education. In cosa consiste il Custom Publishing? McGraw-Hill dispone di un vasto database, "Create", in cui sono contenuti in forma digitale i propri volumi, sia italiani sia stranieri. Con un semplice clic del mouse, il docente può creare il testo per il proprio corso attingendo a singoli capitoli dai più disparati volumi, in modo da realizzare un libro personalizzato che risponda al meglio alle esigenze del proprio corso, con la possibilità di aggiungere anche materiale originale scritto dal docente stesso. Quali sono i vantaggi del Custom Publishing? Per i docenti: I materiali didattici sono ritagliati su misura del corso. Il docente può controllarne i contenuti così come la struttura. È possibile includere materiale proprio, in qualunque lingua. La copertina è personalizzabile con i dettagli del corso, il nome del docente, il logo dell'istituzione ecc. Per gli studenti: Il testo contiene esattamente i contenuti utili per il corso. Gli studenti in un unico volume trovano tutti i materiali necessari e risparmiano tempo, non dovendo raccogliere testi da diverse fonti. Il testo contiene solo ed esclusivamente i materiali del corso, pertanto gli studenti risparmiano sull'acquisto non dovendo pagare per contenuti non utilizzati.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271337 Collegamento a IRIS

2019 An IOT Edge-Fog-Cloud Architecture for Vision Based Pallet Integrity
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Vaira, R.; Pietrini, R.; Pierdicca, R.; Zingaretti, P.; Mancini, A.; Frontoni, E.
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271287 Collegamento a IRIS

2019 A Learning Approach for Informative-Frame Selection in US Rheumatology Images
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Fiorentino, M. C.; Moccia, S.; Cipolletta, E.; Filippucci, E.; Frontoni, E.
Editore: Springer Verlag
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271352 Collegamento a IRIS

2019 Automatic generation of point cloud synthetic dataset for historical building representation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Pierdicca, R.; Mameli, M.; Malinverni, E. S.; Paolanti, M.; Frontoni, E.
Editore: Springer Verlag
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271353 Collegamento a IRIS

2019 Visual and textual sentiment analysis of daily news social media images by deep learning
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Felicetti, A.; Martini, M.; Paolanti, M.; Pierdicca, R.; Frontoni, E.; Zingaretti, P.
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271288 Collegamento a IRIS

2019 Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse Balanced Support Vector Machine
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Autore/i: Bernardini, Michele; Romeo, Luca; Misericordia, Paolo; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: The diagnosis of Type 2 Diabetes (T2D) at an early stage has a key role for an adequate T2D integrated management system and patient's follow-up. Recent years have witnessed an increasing amount of available Electronic Health Record (EHR) data and Machine Learning (ML) techniques have been considerably evolving. However, managing and modeling this amount of information may lead to several challenges such as overfitting, model interpretability and computational cost. Starting from these motivations, we introduced a ML method called Sparse Balanced Support Vector Machine (SB-SVM) for discovering T2D in a novel collected EHR dataset (named FIMMG dataset). In particular, among all the EHR features related to exemptions, examination and drug prescriptions we have selected only those collected before T2D diagnosis from a uniform age group of subjects. We demonstrated the reliability of the introduced approach with respect to other ML and Deep Learning approaches widely employed in the state-of-the-art for solving this task. Results evidence that the SB-SVM overcomes the other state-of-the-art competitors providing the best compromise between predictive performance and computation time. Additionally, the induced sparsity allows to increase the model interpretability, while implicitly managing high dimensional data and the usual unbalanced class distribution.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271331 Collegamento a IRIS

2019 Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks
ANNALS OF BIOMEDICAL ENGINEERING
Autore/i: Casella, Alessandro; Moccia, Sara; Frontoni, Emanuele; Paladini, Dario; De Momi, Elena; Mattos, Leonardo S.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272423 Collegamento a IRIS

2019 DEEP CONVOLUTIONAL NEURAL NETWORKS for SENTIMENT ANALYSIS of CULTURAL HERITAGE
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Autore/i: Paolanti, M.; Pierdicca, R.; Martini, M.; Felicetti, A.; Malinverni, E. S.; Frontoni, E.; Zingaretti, P.
Editore: International Society for Photogrammetry and Remote Sensing
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271294 Collegamento a IRIS

2019 Measuring and Assessing Augmented Reality Potential for Educational Purposes: SmartMarca Project
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Frontoni, E.; Paolanti, M.; Puggioni, M.; Pierdicca, R.; Sasso, M.
Editore: Springer Verlag
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271355 Collegamento a IRIS

2019 eTourism: ICT and its role for tourism management
JOURNAL OF HOSPITALITY AND TOURISM TECHNOLOGY
Autore/i: Pierdicca, R.; Paolanti, M.; Frontoni, E.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271358 Collegamento a IRIS

2019 Semantic 3D Object Maps for Everyday Robotic Retail Inspection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Paolanti, M.; Pierdicca, R.; Martini, M.; Di Stefano, F.; Morbidoni, C.; Mancini, A.; Malinverni, E. S.; Frontoni, E.; Zingaretti, P.
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/270984 Collegamento a IRIS

2019 The First Outstanding 50 Years of “Università Politecnica delle Marche”: Research achievements in Physical Sciences and Engineering
Autore/i: Longhi, S.; Monteriu, A.; Freddi, A.; Frontoni, E.; Germani, M.; Revel, G. M.
Editore: Springer International Publishing
Classificazione: 3 Libro
Abstract: The book describes the significant multidisciplinary research findings at the Università Politecnica delle Marche and the expected future advances. It addresses some of the most dramatic challenges posed by today’s fast-growing, global society and the changes it has caused. It also discusses solutions to improve the wellbeing of human beings. The book covers the main research achievements in the different disciplines of the physical sciences and engineering, as well as several research lines developed at the university’s Faculty of Engineering in the fields of electronic and information engineering, telecommunications, biomedical engineering, mechanical engineering, manufacturing technologies, energy, advanced materials, chemistry, physics of matter, mathematical sciences, geotechnical engineering, circular economy, urban planning, construction engineering, infrastructures and environment protection, technologies and digitization of the built environment and cultural heritage. It highlights the international relevance and multidisciplinarity of research at the university as well as the planned research lines for the next years.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/281290 Collegamento a IRIS

2019 Preface
The First Outstanding 50 Years of “Università Politecnica delle Marche”: Research Achievements in Physical Sciences and Engineering
Autore/i: Longhi, S.; Monteriu, A.; Freddi, A.; Frontoni, E.; Germani, M.; Revel, G. M.
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: Preface of the Book entitled << The First Outstanding 50 Years of “Università Politecnica delle Marche”: Research Achievements in Physical Sciences and Engineering >>
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/281313 Collegamento a IRIS

2019 Towards the Design of a Machine Learning-based Consumer Healthcare Platform powered by Electronic Health Records and measurement of Lifestyle through Smartphone Data
2019 IEEE 23rd International Symposium on Consumer Technologies, ISCT 2019
Autore/i: Ferri, A.; Rosati, R.; Bernardini, M.; Gabrielli, L.; Casaccia, S.; Romeo, L.; Monteriu, A.; Frontoni, E.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The estimation of Biological Age (BA) has been debated for several years and no clear and universal understanding has yet been reached to solve this task. Accordingly, the knowledge of an accurate BA index for each individual may be relevant in various areas including health, economy, social policies and decision making processes. The main contribution of this work is the design of a Machine Learning based-consumer healthcare platform powered by electronic health record data (clinical features) and smartphone data (lifestyle features) in order to estimate a sub-index that is strictly correlated with the BA. Preliminary results extracted from a representative subset of clinical and lifestyle features, highlight the potential of the proposed framework in order to estimate the health and physical status of each subject (in terms of the difference between the predicted Chronological Age and the real Chronological Age). Future work will be conducted to encapsulate more information and validate the predicted BA sub-index.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/277370 Collegamento a IRIS

2019 A business application of RTLS technology in Intelligent Retail Environment: Defining the shopper's preferred path and its segmentation
JOURNAL OF RETAILING AND CONSUMER SERVICES
Autore/i: Ferracuti, N.; Norscini, C.; Frontoni, E.; Gabellini, P.; Paolanti, M.; Placidi, V.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271359 Collegamento a IRIS

2019 People Counting in Crowded Environment and Re-identification
RGB-D Image Analysis and Processing
Autore/i: Frontoni, Emanuele; Paolanti, Marina; Pietrini, Rocco
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271360 Collegamento a IRIS

2019 Identifying the use of a park based on clusters of visitors' movements from mobile phone data
JOURNAL OF SPATIAL INFORMATION SCIENCE
Autore/i: Pierdicca, R.; Paolanti, M.; Vaira, R.; Marcheggiani, E.; Malinverni, E. S.; Frontoni, E.
Classificazione: 1 Contributo su Rivista
Abstract: Planning urban parks is a burdensome task, requiring knowledge of countless variables that are impossible to consider all at the same time. One of these variables is the set of people who use the parks. Despite information and communication technologies being a valuable source of data, a standardized method which enables landscape planners to use such information to design urban parks is still broadly missing. The objective of this study is to design an approach that can identify how an urban green park is used by its visitors in order to provide planners and the managing authorities with a standardized method. The investigation was conducted by exploiting tracking data from an existing mobile application developed for Cardeto Park, an urban green area in the heart of the old town of Ancona, Italy. A trajectory clustering algorithm is used to infer the most common trajectories of visitors, exploiting global positioning system and sensor-based tracks. The data used are made publicly available in an open dataset, which is the first one based on real data in this field. On the basis of these user-generated data, the proposed datadriven approach can determine the mission of the park by processing visitors' trajectories whilst using a mobile application specifically designed for this purpose. The reliability of the clustering method has also been confirmed by an additional statistical analysis. This investigation reveals other important user behavioral patterns or trends.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/276346 Collegamento a IRIS

2019 An event based machine learning framework for predictive maintenance in industry 4.0
Proceedings of the ASME Design Engineering Technical Conference
Autore/i: Calabrese, M.; Cimmino, M.; Manfrin, M.; Fiume, F.; Kapetis, D.; Mengoni, M.; Ceccacci, S.; Frontoni, E.; Paolanti, M.; Carrotta, A.; Toscano, G.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Predictive Maintenance concerns the smart monitoring of machine to avoid possible future failures, since because it is better to intervene before the damage occurs, saving time and money. In this paper, a Predictive Maintenance methodology based on Machine learning approach is presented and it is applied to a real cutting machine, a woodworking machinery in a real industrial group, producing accurate estimations. This kind of strategy is important to deal with maintenance problems given the ever increasing need to reduce downtime and associated costs. The Predictive Maintenance methodology implemented allows dynamical decision rules that have to be considered for maintenance prediction using a combined approach on Azure Machine Learning Studio. The Three models (RF, GBM and XGBM) allowed the accurately predict machine down ever gripped bearing thanks to the pre-processing phases
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/276186 Collegamento a IRIS

2019 From Artificial Intelligence and Databases to Cognitive Computing: Past and Future Computer Engineering Research at UNIVPM
The First Outstanding 50 Years of “Università Politecnica delle Marche”
Autore/i: Cucchiarelli, Alessandro; Diamantini, Claudia; Dragoni, Aldo Franco; Francescangeli, Fabrizio; Frontoni, Emanuele; Mancini, Adriano; Marinelli, Fabrizio; Morbidoni, Christian; Pezzella, Ferdinando; Pisacane, Ornella; Potena, Domenico; Ribighini, Giuseppa; Spalazzi, Luca; Storti, Emanuele; Ursino, Domenico; Vici, Francesco; Zingaretti, Primo
Editore: Springer, Cham
Classificazione: 2 Contributo in Volume
Abstract: In the last decades, Computer Engineering has shown an impressive development and has become a pervasive protagonist in daily life and scientific research. Databases and Artificial Intelligence represent two of the major players in this development. Today, they are quickly converging towards a new, much more sophisticated and inclusive, paradigm, namely Cognitive Computing. This paradigm leverages Big Data and Artificial Intelligence to design approaches and build systems capable of (at least partially) reproducing human brain behavior. In this paradigm, an important role can be also played by Mathematical Programming. Cognitive systems are able to autonomously learn, reason, understand and process a huge amount of extremely varied data. Their ultimate goal is the capability of interacting naturally with their users. In the last 50 years, UNIVPM has played a leading role in scientific research in Databases and Artificial Intelligence, and, thanks to the acquired expertise, is going to play a key role in Cognitive Computing research in the future.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272654 Collegamento a IRIS

2019 Preterm infants’ limb-pose estimation from depth images using convolutional neural networks
IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology
Autore/i: Moccia, Sara; Migliorelli, Lucia; Pietrini, Rocco; Frontoni, Emanuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/268459 Collegamento a IRIS

2019 Accurate modeling of the microwave treatment of works of art
SUSTAINABILITY
Autore/i: Pierdicca, R.; Paolanti, M.; Bacchiani, R.; de Leo, R.; Bisceglia, B.; Frontoni, E.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271362 Collegamento a IRIS

2019 Empowered Optical Inspection by Using Robotic Manipulator in Industrial Applications
IEEE International Conference on Intelligent Robots and Systems
Autore/i: Galdelli, A.; Pagnotta, D. P.; Mancini, A.; Freddi, A.; Monteriu, A.; Frontoni, E.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Nowadays the inspection of products at the end of line represents a critical phase. At this stage, it is necessary to look for defects in order to prevent the quality check to fail, and to provide information for improving the production as well. This task can be performed by using several sensing technologies, and the contactless optical inspection plays a key role. In this regard, the use of advanced robotic manipulators offers the capability to change the viewpoint of a given object and to inspect its multiple faces. We propose an approach that combines the use of photometric stereo to derive a 3D model of objects, empowered by the super-resolution that is applied on the original dataset (upstream) or on the normal images (downstream) in order to increase the quality of the final 3D model. The vision system is mounted on a robotic manipulator, able to grasp and change the viewpoint, thus offering a more complete view of the object to be inspected. The obtained results show that the developed solution increases the quality of the derived 3D models used for inspection tasks on different faces of the objects; this is achieved by using the manipulation ability offered by the adopted robotic platform.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/275366 Collegamento a IRIS

2019 Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0
EXPERT SYSTEMS WITH APPLICATIONS
Autore/i: Romeo, L.; Loncarski, J.; Paolanti, M.; Bocchini, G.; Mancini, A.; Frontoni, E.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271330 Collegamento a IRIS

2019 Deep learning for soil and crop segmentation from remotely sensed data
REMOTE SENSING
Autore/i: Dyson, J.; Mancini, A.; Frontoni, E.; Zingaretti, P.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271290 Collegamento a IRIS

2019 Archaeological landscape and heritage. Innovative knowledge-based dissemination and development strategies in the Distretto Culturale Evoluito Flaminia NextOne
IL CAPITALE CULTURALE
Autore/i: CLINI, Paolo; FRONTONI, Emanuele; QUATTRINI, Ramona; PIERDICCA, ROBERTO; PUGGIONI, MARIAPAOLA
Classificazione: 1 Contributo su Rivista
Abstract: The adoption of dissemination strategies based on Information and Communication Technologies (ICTs) has generated a paradigm shift, empowering users to identify, customize and exploit tourism services. The paper outlines an innovative way to disseminate the archaeological landscape, based on the Flaminia NextOne Distretto Culturale Evoluto (DCE) research project. The contribution summarizes the main achievements of the project in the light of technical improvements in AR applications and mobile cloud management, and also describes the collaborative approach of a public-private partnership. Data analytics from the web platform are also provided, in order to understand the potential and the drawbacks of this methodology. The discussion of the method, the pilot cases and their scalability derive from the main objective of the project which is to promote cultural heritage throughout the territory and to study the socio-economic implications of digital mediation, as discussed in the 2016 L&A En Route seminar. L’adozione di strategie di comunicazione basate su tecnologie dell’informazione e della comunicazione (TIC) ha generato un cambio di paradigma, consentendo agli utenti di identificare, personalizzare e sfruttare i servizi turistici. L’articolo delinea un modo innovativo per veicolare il paesaggio archeologico, basato sul progetto di ricerca Flaminia NextOne Distretto Culturale Evoluto (DCE). Il contributo riassume i principali risultati del progetto alla luce dei miglioramenti tecnici ottenuti nelle applicazioni AR e nella gestione di un cloud dati, nonché nell’approccio collaborativo del partenariato pubblico-privato. Viene fornita anche un’analisi dei dati della piattaforma web, al fine di comprendere le potenzialità e gli svantaggi della metodologia. La discussione del metodo, i casi pilota e la loro scalabilità derivano dal valore intrinseco del progetto per promuovere il patrimonio culturale diffuso sul territorio e studiare le implicazioni socio-economiche della mediazione digitale, come discusso nel seminario L&A En route del 2016.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/267580 Collegamento a IRIS

2018 Health@Home: Pilot cases and preliminary results: Residential sensor network to promote the active aging of real users
MeMeA 2018 - 2018 IEEE International Symposium on Medical Measurements and Applications, Proceedings
Autore/i: Casaccia, Sara; Pietroni, Filippo; Scalise, Lorenzo; Revel, Gian Marco; Monteriù, Andrea; Prist, Mariorosario; Frontoni, Emanuele; Longhi, Sauro
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This study is realized within the framework of the Health@Home Italian project. The focus of this paper is to provide a description of the experimentation in a pilot case in Veneto region (Italy). The integrated residential sensor network (composed by a mix of domotic equipment and biomedical devices), which will allow older people to improve their life-style in their houses, is discussed, together with the apartments selection (i.e. the specifications and requirements) and the users’ recruitment. The authors will also introduce the analysis of the expected results, based on measurements of preliminary data and signals in living lab and the first feedback from the 13 recruited users. The results of the preliminary tests are used to improve the architecture, following the user-acceptance and the data collection. In this phase of the study, the possible services have been hypothesized, and this aspect will be investigated after the end of the experimentation phase (end of 2018)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/262313 Collegamento a IRIS

2018 Convolutional Networks for Semantic Heads Segmentation using Top-View Depth Data in Crowded Environment
Proceedings - International Conference on Pattern Recognition
Autore/i: Liciotti, Daniele; Paolanti, Marina; Pietrini, Rocco; Frontoni, Emanuele; Zingaretti, Primo
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266000 Collegamento a IRIS

2018 User-Centered Predictive Model for Improving Cultural Heritage Augmented Reality Applications: An HMM-Based Approach for Eye-Tracking Data
JOURNAL OF IMAGING
Autore/i: Pierdicca, Roberto; Paolanti, Marina; Naspetti, Simona; Mandolesi, Serena; Zanoli, Raffaele; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: Today, museum visits are perceived as an opportunity for individuals to explore and make up their own minds. The increasing technical capabilities of Augmented Reality (AR) technology have raised audience expectations, advancing the use of mobile AR in cultural heritage (CH) settings. Hence, there is the need to define a criteria, based on users’ preference, able to drive developers and insiders toward a more conscious development of AR-based applications. Starting from previous research (performed to define a protocol for understanding the visual behaviour of subjects looking at paintings), this paper introduces a truly predictive model of the museum visitor’s visual behaviour, measured by an eye tracker. A Hidden Markov Model (HMM) approach is presented, able to predict users’ attention in front of a painting. Furthermore, this research compares users’ behaviour between adults and children, expanding the results to different kind of users, thus providing a reliable approach to eye trajectories. Tests have been conducted defining areas of interest (AOI) and observing the most visited ones, attempting the prediction of subsequent transitions between AOIs. The results demonstrate the effectiveness and suitability of our approach, with performance evaluation values that exceed 90%.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/262486 Collegamento a IRIS

2018 Cyber Physical Systems for Industry 4.0: Towards Real Time Virtual Reality in Smart Manufacturing
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Frontoni, Emanuele; Loncarski, Jelena; Pierdicca, Roberto; Bernardini, Michele; Sasso, Michele
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266394 Collegamento a IRIS

2018 Machine learning-based approaches to analyse and improve the diagnosis of endothelial dysfunction
2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2018
Autore/i: Calamanti, Chiara; Paolanti, Marina; Romeo, Luca; Bernardini, Michele; Frontoni, Emanuele
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266395 Collegamento a IRIS

2018 Use of an energy harvesting smart floor for indoor localization of people
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY
Autore/i: Contigiani, Marco; Frontoni, Emanuele; Mancini, Adriano; Zingaretti, Primo; Gatto, Andrea; Groppo, Riccardo
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/265996 Collegamento a IRIS

2018 Modelling and Forecasting Customer Navigation in Intelligent Retail Environments
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
Autore/i: Paolanti, Marina; Liciotti, Daniele; Pietrini, Rocco; Mancini, Adriano; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266146 Collegamento a IRIS

2018 Real and Virtual Clinical Trials: A Formal Analysis
TOPOI
Autore/i: Osimani, B.; Bertolaso, Marta; Poellinger, Roland; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: Systems biology is an interdisciplinary approach to complex biological problems through modelling, simulation, and systems-level analysis, which is increasingly establishing itself as an alternative and complementary source of knowledge to standards laboratory, clinical and epidemiologic studies in medicine. It has been proposed that such computer simulation and computer-aided modelling techniques could be employed in the setting of clinical testing, in order to support the planning of clinical trials, refine their conduct and reduce the possibility of their failure. According to this view, patient-specific computer models should be used to generate simulated populations, on which new biomedical products can be safely tested. The Avicenna Alliance refers to this methodology as In Silico Clinical Trial (ISCT). In their recently published Roadmap (Viceconti et al., 2016), the Avicenna alliance produced an in-depth examination of the scientific, technological, and societal obstacles that have to be overcome in order to establish a role for the ISCT in medical research. With the present paper we provide an analysis of ISCTs epistemological status, in particular with respect to the gold standard instrument of clinical investigation: Randomized Controlled Trials. We draw on Cartwright's analysis (2011) of RCTs as a basis for a formal analysis of their epistemic value and as a benchmark for investigating ISCTs. Britton et al.'s study (Britton et al., 2013) on the impact of ion current variability on cardiac electrophysiology is used for illustrative purposes.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/257014 Collegamento a IRIS

2018 Energy harvesting applied to smart shoes
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY
Autore/i: Frontoni, Emanuele; Mancini, Adriano; Zingaretti, Primo; Gatto, Andrea
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/265995 Collegamento a IRIS

2018 Improving Variable Rate Treatments by Integrating Aerial and Ground Remotely Sensed Data
2018 International Conference on Unmanned Aircraft Systems, ICUAS 2018
Autore/i: Mancini, Adriano; Frontoni, Emanuele; Zingaretti, Primo
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/265998 Collegamento a IRIS

2018 A Synergic Photometric Stereo and Super Resolution Approach for Optical Inspection
2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2018
Autore/i: Galdelli, Alessandro; Mancini, Adriano; Frontoni, Emanuele; Zingaretti, Primo
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/265997 Collegamento a IRIS

2018 Machine Learning approach for Predictive Maintenance in Industry 4.0
2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2018
Autore/i: Paolanti, M.; Romeo, L.; Felicetti, A.; Mancini, A.; Frontoni, E.; Loncarski, J.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271367 Collegamento a IRIS

2018 Participatory location-based learning and ICT as tools to increase international reputation of a wellbeing destination in rural areas: a case study
Tourism, Health, Wellbeing and Protected Areas
Autore/i: Cavicchi, A.; Frontoni, E.; Pierdicca, R.; Rinaldi, C.; Bertella, G.; Santini, C.
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271372 Collegamento a IRIS

2018 Interconnection strategies of point absorber type wave energy converters and rectifier units
Proceedings of International Conference on Harmonics and Quality of Power, ICHQP
Autore/i: Loncarski, J.; Soman, D. E.; Frontoni, E.
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271368 Collegamento a IRIS

2018 A Survey of Augmented, Virtual, and Mixed Reality for Cultural Heritage
ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE
Autore/i: Bekele Mafkereseb, Kassahun; Pierdicca, Roberto; Frontoni, Emanuele; Malinverni, Eva Savina; Gain, James
Classificazione: 1 Contributo su Rivista
Abstract: A multimedia approach to the diffusion, communication, and exploitation of Cultural Heritage (CH) is a well-established trend worldwide. Several studies demonstrate that the use of new and combined media enhances how culture is experienced. The benefit is in terms of both number of people who can have access to knowledge and the quality of the diffusion of the knowledge itself. In this regard, CH uses augmented-, virtual-, and mixed-reality technologies for different purposes, including education, exhibition enhancement, exploration, reconstruction, and virtual museums. These technologies enable user-centred presentation and make cultural heritage digitally accessible, especially when physical access is constrained. A number of surveys of these emerging technologies have been conducted; however, they are either not domain specific or lack a holistic perspective in that they do not cover all the aspects of the technology. A review of these technologies from a cultural heritage perspective is therefore warranted. Accordingly, our article surveys the state-of-the-art in augmented-, virtual-, and mixed-reality systems as a whole and from a cultural heritage perspective. In addition, we identify specific application areas in digital cultural heritage and make suggestions as to which technology is most appropriate in each case. Finally, the article predicts future research directions for augmented and virtual reality, with a particular focus on interaction interfaces and explores the implications for the cultural heritage domain.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/256362 Collegamento a IRIS

2018 A methodological approach to fully automated highly accelerated life tests
MICROSYSTEM TECHNOLOGIES
Autore/i: Massi, Gionata; Morganti, Gianluca; Claudi, Andrea; Zingaretti, Primo; Mancini, Adriano; Frontoni, Emanuele
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/254077 Collegamento a IRIS

2018 Mechatronic System to Help Visually Impaired Users during Walking and Running
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Autore/i: Mancini, Adriano; Frontoni, Emanuele; Zingaretti, Primo
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/254076 Collegamento a IRIS

2018 An agent-based WCET analysis for top-view person re-identification
CEUR Workshop Proceedings
Autore/i: Paolanti, M.; Placidi, V.; Bernardini, M.; Felicetti, A.; Pietrini, R.; Frontoni, E.
Editore: CEUR-WS
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271346 Collegamento a IRIS

2018 Design and test of a real-time shelf out-of-stock detector system
MICROSYSTEM TECHNOLOGIES
Autore/i: Frontoni, Emanuele; Mancini, Adriano; Zingaretti, Primo; Contigiani, Marco; Bello, Luigi Di; Placidi, Valerio
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/240005 Collegamento a IRIS

2018 A Smart Sensing Architecture for Domestic Monitoring: Methodological Approach and Experimental Validation
SENSORS
Autore/i: Monteriù, Andrea; Prist, Mario; Frontoni, Emanuele; Longhi, Sauro; Pietroni, Filippo; Casaccia, Sara; Scalise, Lorenzo; Cenci, Annalisa; Romeo, Luca; Berta, Riccardo; Pescosolido, Loreto; Orlandi, Gianni; Revel, Gian
Classificazione: 1 Contributo su Rivista
Abstract: Smart homes play a strategic role for improving life quality of people, enabling to monitor people at home with numerous intelligent devices. Sensors can be installed to provide a continuous assistance without limiting the resident’s daily routine, giving her/him greater comfort, well-being and safety. This paper is based on the development of domestic technological solutions to improve the life quality of citizens and monitor the users and the domestic environment, based on features extracted from the collected data. The proposed smart sensing architecture is based on an integrated sensor network to monitor the user and the environment to derive information about the user’s behavior and her/his health status. The proposed platform includes biomedical, wearable, and unobtrusive sensors for monitoring user’s physiological parameters and home automation sensors to obtain information about her/his environment. The sensor network stores the heterogeneous data both locally and remotely in Cloud, where machine learning algorithms and data mining strategies are used for user behavior identification, classification of user health conditions, classification of the smart home profile, and data analytics to implement services for the community. The proposed solution has been experimentally tested in a pilot study based on the development of both sensors and services for elderly users at home.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259196 Collegamento a IRIS

2018 Person re-identification with RGB-D camera in top-view configuration through multiple nearest neighbor classifiers and neighborhood component features selection
SENSORS
Autore/i: Paolanti, Marina; Romeo, Luca; Liciotti, Daniele; Pietrini, Rocco; Cenci, Annalisa; Frontoni, Emanuele; Zingaretti, Primo
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/265999 Collegamento a IRIS

2017 A sensor fusion approach for measuring emotional customer experience in an intelligent retail environment
Consumer Electronics - Berlin (ICCE-Berlin), 2017 IEEE 7th International Conference on
Autore/i: Ciabattoni, Lucio; Frontoni, Emanuele; Liciotti, Daniele; Paolanti, Marina; Romeo, Luca
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/256689 Collegamento a IRIS

2017 Design of an interoperable framework with domotic sensors network integration
Consumer Electronics - Berlin (ICCE-Berlin), 2017 IEEE 7th International Conference on
Autore/i: Frontoni, Emanuele; Liciotti, Daniele; Paolanti, Marina; Pollini, Rama; Zingaretti, Primo
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/256690 Collegamento a IRIS

2017 The use of augmented reality glasses for the application in industry 4.0
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Pierdicca, Roberto; Frontoni, Emanuele; Pollini, Rama; Trani, Matteo; Verdini, Lorenzo
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/256987 Collegamento a IRIS

2017 Movements analysis of preterm infants by using depth sensor
ACM International Conference Proceeding Series
Autore/i: Cenci, Annalisa; Liciotti, Daniele; Frontoni, Emanuele; Zingaretti, Primo; Carnielli, Virgilio Paolo
Editore: Association for Computing Machinery
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/262398 Collegamento a IRIS

2017 Building detection in multi-source aerial data with imbalanced training samples: an approach based on the Bayesian Vector Quantizer
INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION
Autore/i: BENVENUTI, FILIPPO; MANCINI, ADRIANO; POTENA, Domenico; DIAMANTINI, Claudia; FRONTONI, EMANUELE; ZINGARETTI, PRIMO
Classificazione: 1 Contributo su Rivista
Abstract: The problem of building detection in multi-source aerial data has a large variety of applications from map updating to the detection of illegal construction. The development of a capability to integrate multispectral and LiDAR technologies has been proved to be the most effective strategy in dealing with the problem. An automated and combined approach enables such joint capabilities to process data, reducing human effort that is usually limited to the creation of training sets. This aspect plays a key role in getting accurate results and it is central to dealing with the problem of an imbalanced data set. We tailor the Bayesian Vector Quantizer algorithm (BVQ) to the problem of building detection from multi-source high-resolution aerial data like LiDAR with a focus on the imbalanced data set problem. The result is a methodology that is optimised towards the solution of strongly imbalanced problems where noise is present and where the number of training samples for buildings over classes like trees, land and grass is one order of magnitude lower. The formulation is compared with other well-adopted approaches which highlight the relative strengths of the BVQ approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/248350 Collegamento a IRIS

2017 Soil / crop segmentation from remotely sensed data acquired by Unmanned Aerial System
2017 International Conference on Unmanned Aircraft Systems, ICUAS 2017
Autore/i: Mancini, Adriano; Dyson, Jack; Frontoni, Emanuele; Zingaretti, Primo
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/255842 Collegamento a IRIS

2017 Visual and textual sentiment analysis of brand-related social media pictures using deep convolutional neural networks
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Paolanti, Marina; Kaiser, Carolin; Schallner, René; Frontoni, Emanuele; Zingaretti, Primo
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/255845 Collegamento a IRIS

2017 People Detection and Tracking from an RGB-D Camera in Top-View Configuration: Review of Challenges and Applications
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Liciotti, Daniele; Paolanti, Marina; Frontoni, Emanuele; Zingaretti, Primo
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/255843 Collegamento a IRIS

2017 Robotic platform for deep change detection for rail safety and security
2017 European Conference on Mobile Robots, ECMR 2017
Autore/i: Sturari, Mirco; Paolanti, Marina; Frontoni, Emanuele; Mancini, Adriano; Zingaretti, Primo
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/255839 Collegamento a IRIS

2017 Mobile robot for retail surveying and inventory using visual and textual analysis of monocular pictures based on deep learning
2017 European Conference on Mobile Robots, ECMR 2017
Autore/i: Paolanti, Marina; Sturari, Mirco; Mancini, Adriano; Zingaretti, Primo; Frontoni, Emanuele
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/255840 Collegamento a IRIS

2017 HMM-based Activity Recognition with a Ceiling RGB-D Camera
ICPRAM: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS
Autore/i: Duckett, Tom; Bellotto, Nicola; Zingaretti, Primo; Frontoni, Emanuele; Liciotti, Daniele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/256824 Collegamento a IRIS

2017 Pervasive system for consumer behaviour analysis in retail environments
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Autore/i: Liciotti, Daniele; Frontoni, Emanuele; Mancini, Adriano; Zingaretti, Primo
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/246869 Collegamento a IRIS


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