Publications

@article{Baldoni_MTAP_2023,

title = {Definition of guidelines for virtual reality application design based on visual attention},

author = {Sara Baldoni and Mohamed Saifeddine Hadj Sassi and Marco Carli and Federica Battisti},

doi = {10.1007/s11042-023-17488-y},

year  = {2023},

date = {2023-11-03},

urldate = {2023-11-03},

journal = {Multimedia Tools and Applications},

abstract = {In virtual reality applications, head-mounted displays allow users to explore virtual surroundings, thus creating a high sense of immersion. However, due to the novelty of the technology and the possibility of freely enjoying a $$360^circ $$virtual world, users can get distracted and divert their attention from the content of the application. In this work, we define a set of guidelines for the design of virtual reality applications for enhancing the users’ attention. To the best of our knowledge, this is one of the first attempts to provide general guidelines for virtual application design based on visual attention. More specifically, we analyze the different categories of factors that contribute to the user’s responsiveness and define a set of experiments for measuring the user’s promptness with respect to visual stimuli with different features and in the presence of audio/visual distractions. Experimental tests have been carried out with 36 volunteers. The users’ reaction time has been recorded and the performed analysis allowed the definition of a set of guidelines based on individual, operational, and technological factors for the design of virtual reality applications optimized in terms of user attention. In particular, statistical tests demonstrated that the presence of distractions leads to significantly different reaction times with respect to the case of no distractions, and that users belonging to different age intervals have significantly different behaviors. Moreover, the optimal placement of objects has been identified and the impact of cybersickness has been analyzed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mari,

title = {CACTUS: Content-Aware Compression and Transmission Using Semantics for Automotive LiDAR Data},

author = {Daniele Mari and Elena Camuffo and Simone Milani},

editor = {MDPI},

url = {https://www.mdpi.com/1424-8220/23/12/5611},

doi = {10.3390/s23125611},

year  = {2023},

date = {2023-06-15},

urldate = {2023-06-15},

journal = {Sensors},

volume = {23},

issue = {12},

abstract = {Many recent cloud or edge computing strategies for automotive applications require transmitting huge amounts of Light Detection and Ranging (LiDAR) data from terminals to centralized processing units. As a matter of fact, the development of effective Point Cloud (PC) compression strategies that preserve semantic information, which is critical for scene understanding, proves to be crucial. Segmentation and compression have always been treated as two independent tasks; however, since not all the semantic classes are equally important for the end task, this information can be used to guide data transmission. In this paper, we propose Content-Aware Compression and Transmission Using Semantics (CACTUS), which is a coding framework that exploits semantic information to optimize the data transmission, partitioning the original point set into separate data streams. Experimental results show that differently from traditional strategies, the independent coding of semantically consistent point sets preserves class information. Additionally, whenever semantic information needs to be transmitted to the receiver, using the CACTUS strategy leads to gains in terms of compression efficiency, and more in general, it improves the speed and flexibility of the baseline codec used to compress the data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {No-Reference Light Field Image Quality Assessment Exploiting Saliency},

author = {Kamal Lamichhane and Michael Neri and Federica Battisti and Pradip Paudyal and Marco Carli},

url = {https://ieeexplore.ieee.org/document/10091190},

doi = {10.1109/TBC.2023.3242150},

issn = {0018-9316},

year  = {2023},

date = {2023-04-03},

urldate = {2023-04-03},

journal = {IEEE Transactions on Broadcasting},

pages = {1-11},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Testolina2023,

title = {SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints},

author = {Paolo Testolina and Francesco Barbato and Umberto Michieli and Marco Giordani and Pietro Zanuttigh and Michele Zorzi},

doi = {10.1109/tits.2023.3257086},

year  = {2023},

date = {2023-01-01},

journal = {IEEE Transactions on Intelligent Transportation Systems},

pages = {1--13},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brizzi2023,

title = {Selective video enhancement in the Laguerre--Gauss domain},

author = {Michele Brizzi and Federica Battisti and Marco Carli and Alessandro Neri},

year  = {2023},

date = {2023-01-01},

journal = {Signal Processing: Image Communication},

volume = {110},

pages = {116876},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lamichhane2023,

title = {A CNN-based no reference image quality metric exploiting content saliency},

author = {Kamal Lamichhane and Marco Carli and Federica Battisti},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Signal Processing: Image Communication},

volume = {111},

pages = {116899},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Camuffo2023,

title = {Learning from Mistakes: Self-Regularizing Hierarchical Semantic Representations in Point Cloud Segmentation},

author = {Elena Camuffo and Umberto Michieli and Simone Milani},

url = {https://arxiv.org/abs/2301.11145},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {arXiv preprint arXiv:2301.11145},

abstract = {Recent advances in autonomous robotic technologies have highlighted the growing need for precise environmental analysis. LiDAR semantic segmentation has gained attention to accomplish fine-grained scene understanding by acting directly on raw content provided by sensors. Recent solutions showed how different learning techniques can be used to improve the performance of the model, without any architectural or dataset change. Following this trend, we present a coarse-to-fine setup that LEArns from classification mistaKes (LEAK) derived from a standard model. First, classes are clustered into macro groups according to mutual prediction errors; then, the learning process is regularized by: (1) aligning class-conditional prototypical feature representation for both fine and coarse classes, (2) weighting instances with a per-class fairness index. Our LEAK approach is very general and can be seamlessly applied on top of any segmentation architecture; indeed, experimental results showed that it enables state-of-the-art performances on different architectures, datasets and tasks, while ensuring more balanced class-wise results and faster convergence.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{barbato2023continualb,

title = {Continual Road-Scene Semantic Segmentation via Feature-Aligned Symmetric Multi-Modal Network},

author = {Francesco Barbato and Elena Camuffo and Simone Milani and Pietro Zanuttigh},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {arXiv preprint arXiv:2308.04702},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{shenaj2023federated,

title = {Federated Learning in Computer Vision},

author = {Donald Shenaj and Giulia Rizzoli and Pietro Zanuttigh},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {IEEE Access},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rizzoli2023source,

title = {Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers},

author = {Giulia Rizzoli and Donald Shenaj and Pietro Zanuttigh},

year  = {2023},

date = {2023-01-01},

journal = {arXiv preprint arXiv:2305.14269},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Rizzoli2022,

title = {Multimodal Semantic Segmentation in Autonomous Driving: A Review of Current Approaches and Future Perspectives},

author = {Giulia Rizzoli and Francesco Barbato and Pietro Zanuttigh},

doi = {10.3390/technologies10040090},

year  = {2022},

date = {2022-07-01},

journal = {Technologies},

volume = {10},

number = {4},

pages = {90},

publisher = {MDPI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shenaj2022,

title = {Continual coarse-to-fine domain adaptation in semantic segmentation},

author = {Donald Shenaj and Francesco Barbato and Umberto Michieli and Pietro Zanuttigh},

doi = {10.1016/j.imavis.2022.104426},

year  = {2022},

date = {2022-05-01},

journal = {Image and Vision Computing},

volume = {121},

pages = {104426},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Baldoni2022,

title = {Analysis of a 2D Representation for CPS Anomaly Detection in a Context-Based Security Framework},

author = {Sara Baldoni and Marco Carli and Federica Battisti},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Frontiers in Signal Processing},

volume = {1},

pages = {19},

publisher = {Frontiers},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Baldoni2022c,

title = {GNSS-Imaging Data Fusion for Integrity Enhancement in Autonomous Vehicles},

author = {Sara Baldoni and Federica Battisti and Michele Brizzi and Alessandro Neri},

year  = {2022},

date = {2022-01-01},

journal = {IEEE Transactions on Aerospace and Electronic Systems},

volume = {58},

number = {5},

pages = {4690--4704},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sassi2022a,

title = {Simulation-based virtual reality training for firefighters},

author = {Mohamed Saifeddine Hadj Sassi and Federica Battisti and Marco Carli},

year  = {2022},

date = {2022-01-01},

journal = {Electronic Imaging},

volume = {34},

pages = {1--5},

publisher = {Society for Imaging Science and Technology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Arif2022,

title = {Human reaction time in a mixed reality environment},

author = {Syed Muhammad Umair Arif and Michele Brizzi and Marco Carli and Federica Battisti},

year  = {2022},

date = {2022-01-01},

journal = {Frontiers in Neuroscience},

volume = {16},

pages = {1410},

publisher = {Frontiers},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Neri2022,

title = {Sound Event Detection for Human Safety and Security in Noisy Environments},

author = {Michael Neri and Federica Battisti and Alessandro Neri and Marco Carli},

year  = {2022},

date = {2022-01-01},

journal = {IEEE Access},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Eleuch2022,

title = {A study on the impact of multiview distributed feature coding on a multicamera vehicle tracking system at roundabouts},

author = {Salma Eleuch and Nadia Khouja and Simone Milani and Tomaso Erseghe and Fethi Tlili},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {IEEE Access},

volume = {10},

pages = {39502--39517},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nowroozi2022,

title = {Real or Virtual: A Video Conferencing Background Manipulation-Detection System},

author = {Ehsan Nowroozi and Yassine Mekdad and Mauro Conti and Simone Milani and Selcuk Uluagac and Berrin Yanikoglu},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {arXiv preprint arXiv:2204.11853},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{	11577_3454207,

title = {Online Learning for Adaptive Video Streaming in Mobile Networks},

author = { Theodoros Karagkioules and Georgios S. Paschos and Nikolaos Liakopoulos and Attilio Fiandrotti and Dimitrios Tsilimantos and Marco Cagnazzo},

url = {https://dl.acm.org/doi/10.1145/3460819},

doi = {10.1145/3460819},

year  = {2022},

date = {2022-01-01},

journal = {ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS AND APPLICATIONS},

volume = {18},

number = {1},

pages = {1--22},

publisher = {ACM},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pretto2022,

title = {A Workflow and Digital Filters for Correcting Speed and Equalization Errors on Digitized Audio Open-Reel Magnetic Tapes},

author = {Niccol`o Pretto and Nadir Dalla Pozza and Alberto Padoan and Anthony Chmiel and Kurt James Werner and Alessandra Micalizzi and Emery Schubert and Antonio Roda and Simone Milani and Sergio Canazza},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Journal of the Audio Engineering Society},

volume = {70},

number = {6},

pages = {495--509},

publisher = {Audio Engineering Society},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jung2022housecat6d,

title = {HouseCat6D--A Large-Scale Multi-Modal Category Level 6D Object Pose Dataset with Household Objects in Realistic Scenarios},

author = {HyunJun Jung and Shun-Cheng Wu and Patrick Ruhkamp and Hannah Schieber and Pengyuan Wang and Giulia Rizzoli and Hongcheng Zhao and Sven Damian Meier and Daniel Roth and Nassir Navab and others},

year  = {2022},

date = {2022-01-01},

journal = {arXiv preprint arXiv:2212.10428},

keywords = {},

pubstate = {published},

tppubtype = {article}

}