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Author Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera edit  openurl
  Title (down) Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation Type Conference Article
  Year 2020 Publication Bioimage computation workshop Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address Virtual; August 2020  
  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 HUPBA Approved no  
  Call Number Admin @ si @ AAF2020 Serial 3520  
Permanent link to this record
 

 
Author Raquel Justo; Leila Ben Letaifa; Cristina Palmero; Eduardo Gonzalez-Fraile; Anna Torp Johansen; Alain Vazquez; Gennaro Cordasco; Stephan Schlogl; Begoña Fernandez-Ruanova; Micaela Silva; Sergio Escalera; Mikel de Velasco; Joffre Tenorio-Laranga; Anna Esposito; Maria Korsnes; M. Ines Torres edit  url
openurl 
  Title (down) Analysis of the Interaction between Elderly People and a Simulated Virtual Coach, Journal of Ambient Intelligence and Humanized Computing Type Journal Article
  Year 2020 Publication Journal of Ambient Intelligence and Humanized Computing Abbreviated Journal AIHC  
  Volume 11 Issue 12 Pages 6125-6140  
  Keywords  
  Abstract The EMPATHIC project develops and validates new interaction paradigms for personalized virtual coaches (VC) to promote healthy and independent aging. To this end, the work presented in this paper is aimed to analyze the interaction between the EMPATHIC-VC and the users. One of the goals of the project is to ensure an end-user driven design, involving senior users from the beginning and during each phase of the project. Thus, the paper focuses on some sessions where the seniors carried out interactions with a Wizard of Oz driven, simulated system. A coaching strategy based on the GROW model was used throughout these sessions so as to guide interactions and engage the elderly with the goals of the project. In this interaction framework, both the human and the system behavior were analyzed. The way the wizard implements the GROW coaching strategy is a key aspect of the system behavior during the interaction. The language used by the virtual agent as well as his or her physical aspect are also important cues that were analyzed. Regarding the user behavior, the vocal communication provides information about the speaker’s emotional status, that is closely related to human behavior and which can be extracted from the speech and language analysis. In the same way, the analysis of the facial expression, gazes and gestures can provide information on the non verbal human communication even when the user is not talking. In addition, in order to engage senior users, their preferences and likes had to be considered. To this end, the effect of the VC on the users was gathered by means of direct questionnaires. These analyses have shown a positive and calm behavior of users when interacting with the simulated virtual coach as well as some difficulties of the system to develop the proposed coaching strategy.  
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  Notes HuPBA; no proj Approved no  
  Call Number Admin @ si @ JLP2020 Serial 3443  
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Author Alejandro Cartas; Petia Radeva; Mariella Dimiccoli edit  url
doi  openurl
  Title (down) Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications Type Journal Article
  Year 2020 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume 8 Issue Pages 77344 - 77363  
  Keywords  
  Abstract Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications.  
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  Area Expedition Conference  
  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ CRD2020 Serial 3436  
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Author Yi Xiao; Felipe Codevilla; Christopher Pal; Antonio Lopez edit   pdf
openurl 
  Title (down) Action-Based Representation Learning for Autonomous Driving Type Conference Article
  Year 2020 Publication Conference on Robot Learning Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet).  
  Address virtual; November 2020  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference CORL  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ XCP2020 Serial 3487  
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Author Jialuo Chen; M.A.Souibgui; Alicia Fornes; Beata Megyesi edit   pdf
openurl 
  Title (down) A Web-based Interactive Transcription Tool for Encrypted Manuscripts Type Conference Article
  Year 2020 Publication 3rd International Conference on Historical Cryptology Abbreviated Journal  
  Volume Issue Pages 52-59  
  Keywords  
  Abstract Manual transcription of handwritten text is a time consuming task. In the case of encrypted manuscripts, the recognition is even more complex due to the huge variety of alphabets and symbol sets. To speed up and ease this process, we present a web-based tool aimed to (semi)-automatically transcribe the encrypted sources. The user uploads one or several images of the desired encrypted document(s) as input, and the system returns the transcription(s). This process is carried out in an interactive fashion with
the user to obtain more accurate results. For discovering and testing, the developed web tool is freely available.
 
  Address Virtual; June 2020  
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  ISSN ISBN Medium  
  Area Expedition Conference HistoCrypt  
  Notes DAG; 600.140; 602.230; 600.121 Approved no  
  Call Number Admin @ si @ CSF2020 Serial 3447  
Permanent link to this record
 

 
Author Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados edit   pdf
openurl 
  Title (down) A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages Type Journal Article
  Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 136 Issue Pages 219-227  
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  Abstract In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks.  
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  Notes DAG; 600.140; 601.311; 600.121 Approved no  
  Call Number Admin @ si @ CFV2020 Serial 3451  
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Author Debora Gil; Guillermo Torres edit   pdf
openurl 
  Title (down) A multi-shape loss function with adaptive class balancing for the segmentation of lung structures Type Conference Article
  Year 2020 Publication 34th International Congress and Exhibition on Computer Assisted Radiology & Surgery Abbreviated Journal  
  Volume Issue Pages  
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  Address Virtual; June 2020  
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  Area Expedition Conference CARS  
  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ GiT2020 Serial 3472  
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Author Guillermo Torres; Debora Gil edit  openurl
  Title (down) A multi-shape loss function with adaptive class balancing for the segmentation of lung structures Type Journal Article
  Year 2020 Publication International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCAR  
  Volume 15 Issue 1 Pages S154-55  
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  Notes IAM Approved no  
  Call Number Admin @ si @ ToG2020 Serial 3590  
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Author Oriol Ramos Terrades; Albert Berenguel; Debora Gil edit   pdf
url  openurl
  Title (down) A flexible outlier detector based on a topology given by graph communities Type Miscellaneous
  Year 2020 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings.  
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  Notes IAM; DAG; 600.139; 600.145; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RBG2020 Serial 3475  
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Author Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes edit   pdf
openurl 
  Title (down) A conditional GAN based approach for distorted camera captured documents recovery Type Conference Article
  Year 2020 Publication 4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence Abbreviated Journal  
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  Address Virtual; December 2020  
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  ISSN ISBN Medium  
  Area Expedition Conference MedPRAI  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ SKF2020 Serial 3450  
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Author Lluis Gomez; Anguelos Nicolaou; Marçal Rusiñol; Dimosthenis Karatzas edit  openurl
  Title (down) 12 years of ICDAR Robust Reading Competitions: The evolution of reading systems for unconstrained text understanding Type Book Chapter
  Year 2020 Publication Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis Abbreviated Journal  
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  Publisher Springer Place of Publication Editor K. Alahari; C.V. Jawahar  
  Language Summary Language Original Title  
  Series Editor Series Title Series on Advances in Computer Vision and Pattern Recognition Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.121 Approved no  
  Call Number GNR2020 Serial 3494  
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