VIS - Visió Artificial i Sistemes Intel·ligents
Recerca bàsica i aplicada al desenvolupament de sistemes intel•ligents capaços d'interactuar amb el món de forma autònoma i ubica. Aquests sistemes hauran de percebre, raonar, planificar, actuar i aprendre de l'experiència prèvia. El grup treballa activament en tres àrees: robòtica social i aèria ; visió per computador i reconeixement estructural de patrons. Dins de la robòtica social s’està treballant en els següents temes: Interacció robot-humà; localització i navegació social i autònoma de robots; localització i construcció simultània de mapes (SLAM); robòtica ubiqua; robòtica mòbil cooperativa; i robòtica aèria. En visió per computador es treballa en: seguiment, identificació i reconeixement de objectes; en xarxes de sensors de càmeres; fusió de dades; i percepció cooperativa. I en reconeixement estructural de patrons s'està treballant en mètodes de síntesi i coincidència de grafs i el la seva aplicació a la robòtica.
The Artificial Vision and Intelligent Systems Group (VIS) carries out basic and applied research with the aim of understanding and designing intelligent systems that are capable of interacting with the real world in an autonomous and wide-reaching manner. Such intelligent systems must perceive, reason, plan, act and learn from previous experiences. The group works on the following topics: robust colour image segmentation and labelling, pattern recognition, viewpoint invariant object learning and recognition, object tracking, face tracking, biometrics, processing and analysis of medical images for diagnosis, document analysis, mobile robot navigation, simultaneous localisation and map building, visual servoing, and human-computer interaction. The possible areas of application of the VIS?s research include the automotive and transport industry, the biomedical imaging industry, the space industry, robotics applications, security, home and office automation, the entertainment industry, and future computing enviro
The Artificial Vision and Intelligent Systems Group (VIS) carries out basic and applied research with the aim of understanding and designing intelligent systems that are capable of interacting with the real world in an autonomous and wide-reaching manner. Such intelligent systems must perceive, reason, plan, act and learn from previous experiences. The group works on the following topics: robust colour image segmentation and labelling, pattern recognition, viewpoint invariant object learning and recognition, object tracking, face tracking, biometrics, processing and analysis of medical images for diagnosis, document analysis, mobile robot navigation, simultaneous localisation and map building, visual servoing, and human-computer interaction. The possible areas of application of the VIS?s research include the automotive and transport industry, the biomedical imaging industry, the space industry, robotics applications, security, home and office automation, the entertainment industry, and future computing enviro
Collections in this community
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Altres [2]
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Articles de revista [128]
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Capítols de llibre [22]
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Presentacions [2]
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Reports de recerca [12]
Recent Submissions
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WOLF: A modular estimation framework for robotics based on factor graphs
(Institute of Electrical and Electronics Engineers (IEEE), 2022)
Article
Open AccessThis paper introduces WOLF, a C++ estimation framework based on factor graphs and targeted at mobile robotics. WOLF can be used beyond SLAM to handle self-calibration, model identification, or the observation of dynamic ... -
Box-Jenkins autoregressive models for PEMFC operating under dynamical conditions
(2021)
Conference report
Open AccessThe objective of the present work is to explore and validate autoregressive, control oriented models models Proton Exchange Membrane Fuel Cells coperatinf under dynamic condditions. Autoregressive models have several ... -
Trends and Characteristics of Emergency Medical Services in Italy: A 5-Years Population-Based Registry Analysis
(2020-12-11)
Article
Open AccessBackground: Emergency Medical Services (EMS) plays a fundamental role in providing good quality healthcare services to citizens, as they are the first responders in distressing situations. Few studies have used available ... -
PhysXNet: a customizable approach for learning cloth dynamics on dressed people
(Institute of Electrical and Electronics Engineers (IEEE), 2021)
Conference report
Open AccessWe introduce PhysXNet, a learning-based approach to predict the dynamics of deformable clothes given 3D skeleton motion sequences of humans wearing these clothes. The proposed model is adaptable to a large variety of ... -
Body size and depth disambiguation in multi-person reconstruction from single images
(Institute of Electrical and Electronics Engineers (IEEE), 2021)
Conference report
Open AccessWe address the problem of multi-person 3D body pose and shape estimation from a single image. While this problem can be addressed by applying single-person approaches multiple times for the same scene, recent works have ... -
Dual-branch CNNs for vehicle detection and tracking on LiDAR data
(2021-11-01)
Article
Open AccessWe present a novel vehicle detection and tracking system that works solely on 3D LiDAR information. Our approach segments vehicles using a dual-view representation of the 3D LiDAR point cloud on two independently trained ... -
Heuri: a Scrabble© playing engine using a probability-based heuristic
(2022-02-16)
Article
Open AccessThe game of Scrabble has been successfully tackled by two engines: Quackle and Maven. They attain the state-of-the-art in Computer Scrabble. These engines use simulation techniques and precalculated values to achieve ... -
SMPLicit: Topology-aware generative model for clothed people
(Institute of Electrical and Electronics Engineers (IEEE), 2021)
Conference report
Open AccessIn this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. In contrast to existing learning-based approaches that require training specific models for each ... -
D-NeRF: neural radiance fields for dynamic scenes
(Institute of Electrical and Electronics Engineers (IEEE), 2021)
Conference report
Open AccessNeural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. Among these, stands ... -
Automated chlorine dosage in a simulated drinking water treatment plant: a real case study
(Multidisciplinary Digital Publishing Institute (MDPI), 2021-11)
Part of book or chapter of book
Open AccessIn recent decades, increasing attention has been paid to the sustainability of products and processes, including activities aimed at environmental protection, site reclamation or treatment of contaminated ef¿uents, as well ... -
Human-robot collaborative multi-agent path planning using Monte Carlo tree search and social reward sources
(2021)
Conference report
Open AccessThe collaboration between humans and robots in an object search task requires the achievement of shared plans obtained from communicating and negotiating. In this work, we assume that the robot computes, as a first step, ... -
A hybrid visual-based SLAM architecture: local filter-based SLAM with keyframe-based global mapping
(Multidisciplinary Digital Publishing Institute (MDPI), 2021-12-29)
Article
Open AccessThis work presents a hybrid visual-based SLAM architecture that aims to take advantage of the strengths of each of the two main methodologies currently available for implementing visual-based SLAM systems, while at the ...
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