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Author David Curto; Albert Clapes; Javier Selva; Sorina Smeureanu; Julio C. S. Jacques Junior; David Gallardo-Pujol; Georgina Guilera; David Leiva; Thomas B. Moeslund; Sergio Escalera; Cristina Palmero
Title (up) Dyadformer: A Multi-Modal Transformer for Long-Range Modeling of Dyadic Interactions Type Conference Article
Year 2021 Publication IEEE/CVF International Conference on Computer Vision Workshops Abbreviated Journal
Volume Issue Pages 2177-2188
Keywords
Abstract Personality computing has become an emerging topic in computer vision, due to the wide range of applications it can be used for. However, most works on the topic have focused on analyzing the individual, even when applied to interaction scenarios, and for short periods of time. To address these limitations, we present the Dyadformer, a novel multi-modal multi-subject Transformer architecture to model individual and interpersonal features in dyadic interactions using variable time windows, thus allowing the capture of long-term interdependencies. Our proposed cross-subject layer allows the network to explicitly model interactions among subjects through attentional operations. This proof-of-concept approach shows how multi-modality and joint modeling of both interactants for longer periods of time helps to predict individual attributes. With Dyadformer, we improve state-of-the-art self-reported personality inference results on individual subjects on the UDIVA v0.5 dataset.
Address Virtual; October 2021
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference ICCVW
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ CCS2021 Serial 3648
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Author Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera
Title (up) Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps Type Journal Article
Year 2019 Publication IEEE Transactions on Circuits and Systems for Video Technology Abbreviated Journal TCSVT
Volume 29 Issue 6 Pages 1729-1740
Keywords Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding
Abstract Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset.
Address June 2019,
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ AAK2018 Serial 3213
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Author Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez; Antonio Lopez; C. Elvira
Title (up) Dynamic Comparison of Headlights Type Journal Article
Year 2008 Publication Journal of Automobile Engineering Abbreviated Journal
Volume 222 Issue 5 Pages 643–656
Keywords video alignment
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ SDL2008a Serial 958
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Author Bogdan Raducanu; Fadi Dornaika
Title (up) Dynamic Facial Expression Recognition Using Laplacian Eigenmaps-Based Manifold Learning Type Conference Article
Year 2010 Publication IEEE International Conference on Robotics and Automation Abbreviated Journal
Volume Issue Pages 156–161
Keywords
Abstract In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are: (i) a view- and texture independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker; (ii) the complexity of the non-linear facial expression space is modelled through a manifold, whose structure is learned using Laplacian Eigenmaps. The projected facial expressions are afterwards recognized based on Nearest Neighbor classifier; (iii) with the proposed approach, we developed an application for an AIBO robot, in which it mirrors the perceived facial expression.
Address Anchorage; AK; USA;
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1050-4729 ISBN 978-1-4244-5038-1 Medium
Area Expedition Conference ICRA
Notes OR; MV Approved no
Call Number BCNPCL @ bcnpcl @ RaD2010 Serial 1310
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Author Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas
Title (up) Dynamic Lexicon Generation for Natural Scene Images Type Conference Article
Year 2016 Publication 14th European Conference on Computer Vision Workshops Abbreviated Journal
Volume Issue Pages 395-410
Keywords scene text; photo OCR; scene understanding; lexicon generation; topic modeling; CNN
Abstract Many scene text understanding methods approach the endtoend recognition problem from a word-spotting perspective and take huge bene t from using small per-image lexicons. Such customized lexicons are normally assumed as given and their source is rarely discussed.
In this paper we propose a method that generates contextualized lexicons
for scene images using only visual information. For this, we exploit
the correlation between visual and textual information in a dataset consisting
of images and textual content associated with them. Using the topic modeling framework to discover a set of latent topics in such a dataset allows us to re-rank a xed dictionary in a way that prioritizes the words that are more likely to appear in a given image. Moreover, we train a CNN that is able to reproduce those word rankings but using only the image raw pixels as input. We demonstrate that the quality of the automatically obtained custom lexicons is superior to a generic frequency-based baseline.
Address Amsterdam; The Netherlands; October 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCVW
Notes DAG; 600.084 Approved no
Call Number Admin @ si @ PGR2016 Serial 2825
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Author Kai Wang; Fei Yang; Shiqi Yang; Muhammad Atif Butt; Joost Van de Weijer
Title (up) Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing Type Conference Article
Year 2023 Publication 37th Annual Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Poster
Address New Orleans; USA; December 2023
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference NEURIPS
Notes LAMP Approved no
Call Number Admin @ si @ WYY2023 Serial 3935
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Author Bogdan Raducanu; Fadi Dornaika
Title (up) Dynamic Vs. Static Recognition of Facial Expressions Type Book Chapter
Year 2008 Publication Ambient Intelligence. European Conference Abbreviated Journal
Volume 5355 Issue Pages 13–25
Keywords
Abstract
Address Nuremberg (Germany)
Corporate Author Thesis
Publisher Place of Publication Editor Rabuñal
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference AMI
Notes OR; MV Approved no
Call Number BCNPCL @ bcnpcl @ RaD2008 Serial 1035
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Author Arash Akbarinia; C. Alejandro Parraga
Title (up) Dynamically Adjusted Surround Contrast Enhances Boundary Detection, European Conference on Visual Perception Type Conference Article
Year 2016 Publication European Conference on Visual Perception Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona; Spain; August 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference ECVP
Notes NEUROBIT Approved no
Call Number Admin @ si @ AkP2016b Serial 2900
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Author Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero
Title (up) e-Counterfeit: a mobile-server platform for document counterfeit detection Type Conference Article
Year 2017 Publication 14th IAPR International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms.
Address Kyoto; Japan; November 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.061; 600.097; 600.121 Approved no
Call Number Admin @ si @ BRL2018 Serial 3084
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Author Pau Riba; Jon Almazan; Alicia Fornes; David Fernandez; Ernest Valveny; Josep Llados
Title (up) e-Crowds: a mobile platform for browsing and searching in historical demographyrelated manuscripts Type Conference Article
Year 2014 Publication 14th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages 228 - 233
Keywords
Abstract This paper presents a prototype system running on portable devices for browsing and word searching through historical handwritten document collections. The platform adapts the paradigm of eBook reading, where the narrative is not necessarily sequential, but centered on the user actions. The novelty is to replace digitally born books by digitized historical manuscripts of marriage licenses, so document analysis tasks are required in the browser. With an active reading paradigm, the user can cast queries of people names, so he/she can implicitly follow genealogical links. In addition, the system allows combined searches: the user can refine a search by adding more words to search. As a second contribution, the retrieval functionality involves as a core technology a word spotting module with an unified approach, which allows combined query searches, and also two input modalities: query-by-example, and query-by-string.
Address Creete Island; Grecia; September 2014
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2167-6445 ISBN 978-1-4799-4335-7 Medium
Area Expedition Conference ICFHR
Notes DAG; 600.056; 600.045; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ RAF2014 Serial 2463
Permanent link to this record
 
 
Author Debora Gil; Aura Hernandez-Sabate; Julien Enconniere; Saryani Asmayawati; Pau Folch; Juan Borrego-Carazo; Miquel Angel Piera
Title (up) E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights Type Journal Article
Year 2022 Publication IEEE Access Abbreviated Journal ACCESS
Volume 10 Issue Pages 7489-7503
Keywords
Abstract More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event.
This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches.
Address
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Notes IAM; 600.139; 600.118; 600.145 Approved no
Call Number Admin @ si @ GHE2022 Serial 3721
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Author Debora Gil; Katerine Diaz; Carles Sanchez; Aura Hernandez-Sabate
Title (up) Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images Type Miscellaneous
Year 2020 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Future SARS-CoV-2 virus outbreak COVID-XX might possibly occur during the next years. However the pathology in humans is so recent that many clinical aspects, like early detection of complications, side effects after recovery or early screening, are currently unknown. In spite of the number of cases of COVID-19, its rapid spread putting many sanitary systems in the edge of collapse has hindered proper collection and analysis of the data related to COVID-19 clinical aspects. We describe an interdisciplinary initiative that integrates clinical research, with image diagnostics and the use of new technologies such as artificial intelligence and radiomics with the aim of clarifying some of SARS-CoV-2 open questions. The whole initiative addresses 3 main points: 1) collection of standardize data including images, clinical data and analytics; 2) COVID-19 screening for its early diagnosis at primary care centers; 3) define radiomic signatures of COVID-19 evolution and associated pathologies for the early treatment of complications. In particular, in this paper we present a general overview of the project, the experimental design and first results of X-ray COVID-19 detection using a classic approach based on HoG and feature selection. Our experiments include a comparison to some recent methods for COVID-19 screening in X-Ray and an exploratory analysis of the feasibility of X-Ray COVID-19 screening. Results show that classic approaches can outperform deep-learning methods in this experimental setting, indicate the feasibility of early COVID-19 screening and that non-COVID infiltration is the group of patients most similar to COVID-19 in terms of radiological description of X-ray. Therefore, an efficient COVID-19 screening should be complemented with other clinical data to better discriminate these cases.
Address
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Notes IAM; 600.139; 600.145; 601.337 Approved no
Call Number Admin @ si @ GDS2020 Serial 3474
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Author Asma Bensalah; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados
Title (up) Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis Type Conference Article
Year 2022 Publication Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 Abbreviated Journal
Volume 13424 Issue Pages 336-348
Keywords Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk
Abstract Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case.
Address June 7-9, 2022, Las Palmas de Gran Canaria, Spain
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference IGS
Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no
Call Number Admin @ si @ BFC2022 Serial 3738
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Author Estefania Talavera; Andreea Glavan; Alina Matei; Petia Radeva
Title (up) Eating Habits Discovery in Egocentric Photo-streams Type Miscellaneous
Year 2020 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract CoRR abs/2009.07646
Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioural pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals.
Address
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Notes MILAB Approved no
Call Number Admin @ si @ TGM2020 Serial 3536
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Author V. Valev; Petia Radeva
Title (up) ECG Recognition by Non-Reducible Descriptors. Type Miscellaneous
Year 1995 Publication Portuguese Conference on Pattern Recognition. Abbreviated Journal
Volume Issue Pages
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Abstract
Address Lisbon, Portugal
Corporate Author Thesis
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ VaR1995a Serial 139
Permanent link to this record