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Author (down) Craig Von Land; Ricardo Toledo; Juan J. Villanueva
Title TeleRegions: Application of Telematics in Cardiac Care. Type Miscellaneous
Year 1997 Publication Computers in Cardiology, 1997. Piscataway, NJ: IEEE Computer Society Press, 24: 645–8. Abbreviated Journal
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Notes ADAS Approved no
Call Number ISE @ ise @ VTV1997 Serial 64
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Author (down) Craig Von Land; Ricardo Toledo; Juan J. Villanueva
Title CARE: Computer Assisted Radiology Environment Type Miscellaneous
Year 1996 Publication Tecnologia de Imagenes Medicas, Convencion Iberoamericana sobre la Salud en la Sociedad Global de la Informacion. Abbreviated Journal
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Address Buenos Aires, Argentina
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Notes ADAS Approved no
Call Number ISE @ ise @ VTV1996a Serial 99
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Author (down) Craig Von Land; Ricardo Toledo; Juan J. Villanueva
Title TeleRegion: Tele-Applications for European Regions Type Miscellaneous
Year 1996 Publication Experiencias de validacion, uso y expansion de la telematica a nivel regional e inter–regional. Convencion Iberoamericana sobre la Salud en la Sociedad Global de la Informacion. Abbreviated Journal
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Address Buenos Aires, Argentina
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Notes ADAS Approved no
Call Number ISE @ ise @ VTV1996b Serial 101
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Author (down) Craig Von Land; Ricardo Toledo; Juan J. Villanueva
Title Object Oriented Design of the DICOM standard Type Miscellaneous
Year 1996 Publication International Symposium on Cardiovascular Imaging. Abbreviated Journal
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Address Leiden, The Netherlands
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Notes ADAS Approved no
Call Number ISE @ ise @ VTV1996c Serial 104
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Author (down) Corina Krauter; Ursula Reiter; Albrecht Schmidt; Marc Masana; Rudolf Stollberger; Michael Fuchsjager; Gert Reiter
Title Objective extraction of the temporal evolution of the mitral valve vortex ring from 4D flow MRI Type Conference Article
Year 2019 Publication 27th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine Abbreviated Journal
Volume Issue Pages
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Abstract The mitral valve vortex ring is a promising flow structure for analysis of diastolic function, however, methods for objective extraction of its formation to dissolution are lacking. We present a novel algorithm for objective extraction of the temporal evolution of the mitral valve vortex ring from magnetic resonance 4D flow data and validated the method against visual analysis. The algorithm successfully extracted mitral valve vortex rings during both early- and late-diastolic filling and agreed substantially with visual assessment. Early-diastolic mitral valve vortex ring properties differed between healthy subjects and patients with ischemic heart disease.
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Area Expedition Conference ISMRM
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ KRS2019 Serial 3300
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Author (down) Clementine Decamps; Alexis Arnaud; Florent Petitprez; Mira Ayadi; Aurelia Baures; Lucile Armenoult; Sergio Escalera; Isabelle Guyon; Remy Nicolle; Richard Tomasini; Aurelien de Reynies; Jerome Cros; Yuna Blum; Magali Richard
Title DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification Type Journal Article
Year 2021 Publication BMC Bioinformatics Abbreviated Journal
Volume 22 Issue Pages 473
Keywords
Abstract Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ DAP2021 Serial 3650
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Author (down) Clement Guerin; Christophe Rigaud; Karell Bertet; Jean-Christophe Burie; Arnaud Revel ; Jean-Marc Ogier
Title Réduction de l’espace de recherche pour les personnages de bandes dessinées Type Conference Article
Year 2014 Publication 19th National Congress Reconnaissance de Formes et l'Intelligence Artificielle Abbreviated Journal
Volume Issue Pages
Keywords contextual search; document analysis; comics characters
Abstract Les bandes dessinées représentent un patrimoine culturel important dans de nombreux pays et leur numérisation massive offre la possibilité d'effectuer des recherches dans le contenu des images. À ce jour, ce sont principalement les structures des pages et leurs contenus textuels qui ont été étudiés, peu de travaux portent sur le contenu graphique. Nous proposons de nous appuyer sur des éléments déjà étudiés tels que la position des cases et des bulles, pour réduire l'espace de recherche et localiser les personnages en fonction de la queue des bulles. L'évaluation de nos différentes contributions à partir de la base eBDtheque montre un taux de détection des queues de bulle de 81.2%, de localisation des personnages allant jusqu'à 85% et un gain d'espace de recherche de plus de 50%.
Address Rouen; Francia; July 2014
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Area Expedition Conference RFIA
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ GRB2014 Serial 2480
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Author (down) Claudio Baecchi; Francesco Turchini; Lorenzo Seidenari; Andrew Bagdanov; Alberto del Bimbo
Title Fisher vectors over random density forest for object recognition Type Conference Article
Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 4328-4333
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Address Stockholm; Sweden; August 2014
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ISSN ISBN Medium
Area Expedition Conference ICPR
Notes LAMP; 600.079 Approved no
Call Number Admin @ si @ BTS2014 Serial 2518
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Author (down) Claudia Greco; Carmela Buono; Pau Buch-Cardona; Gennaro Cordasco; Sergio Escalera; Anna Esposito; Anais Fernandez; Daria Kyslitska; Maria Stylianou Kornes; Cristina Palmero; Jofre Tenorio Laranga; Anna Torp Johansen; Maria Ines Torres
Title Emotional Features of Interactions With Empathic Agents Type Conference Article
Year 2021 Publication IEEE/CVF International Conference on Computer Vision Workshops Abbreviated Journal
Volume Issue Pages 2168-2176
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Abstract The current study is part of the EMPATHIC project, whose aim is to develop an Empathic Virtual Coach (VC) capable of promoting healthy and independent aging. To this end, the VC needs to be capable of perceiving the emotional states of users and adjusting its behaviour during the interactions according to what the users are experiencing in terms of emotions and comfort. Thus, the present work focuses on some sessions where elderly users of three different countries interact with a simulated system. Audio and video information extracted from these sessions were examined by external observers to assess participants' emotional experience with the EMPATHIC-VC in terms of categorical and dimensional assessment of emotions. Analyses were conducted on the emotional labels assigned by the external observers while participants were engaged in two different scenarios: a generic one, where the interaction was carried out with no intention to discuss a specific topic, and a nutrition one, aimed to accomplish a conversation on users' nutritional habits. Results of analyses performed on both audio and video data revealed that the EMPATHIC coach did not elicit negative feelings in the users. Indeed, users from all countries have shown relaxed and positive behavior when interacting with the simulated VC during both scenarios. Overall, the EMPATHIC-VC was capable to offer an enjoyable experience without eliciting negative feelings in the users. This supports the hypothesis that an Empathic Virtual Coach capable of considering users' expectations and emotional states could support elderly people in daily life activities and help them to remain independent.
Address VIRTUAL; October 2021
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Area Expedition Conference ICCVW
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ GBB2021 Serial 3647
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Author (down) Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez
Title Computing the Testing Error Without a Testing Set Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
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Abstract Oral. Paper award nominee.
Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trailand-error. That is, typically, researchers will derive many DNN architectures (i.e., topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-thetesting-data is almost impossible. Using a sequestered testing dataset may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset. Specifically, we derive a number of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. This allows us to compute the DNN’s testing error on unseen samples, even when we do not have access to them. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach.
Address Virtual CVPR
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Notes HuPBA; no proj Approved no
Call Number Admin @ si @ CEM2020 Serial 3437
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Author (down) Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez
Title Explainable Early Stopping for Action Unit Recognition Type Conference Article
Year 2020 Publication Faces and Gestures in E-health and welfare workshop Abbreviated Journal
Volume Issue Pages 693-699
Keywords
Abstract A common technique to avoid overfitting when training deep neural networks (DNN) is to monitor the performance in a dedicated validation data partition and to stop
training as soon as it saturates. This only focuses on what the model does, while completely ignoring what happens inside it.
In this work, we open the “black-box” of DNN in order to perform early stopping. We propose to use a novel theoretical framework that analyses meso-scale patterns in the topology of the functional graph of a network while it trains. Based on it,
we decide when it transitions from learning towards overfitting in a more explainable way. We exemplify the benefits of this approach on a state-of-the art custom DNN that jointly learns local representations and label structure employing an ensemble of dedicated subnetworks. We show that it is practically equivalent in performance to early stopping with patience, the standard early stopping algorithm in the literature. This proves beneficial for AU recognition performance and provides new insights into how learning of AUs occurs in DNNs.
Address Virtual; November 2020
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Area Expedition Conference FGW
Notes HUPBA; Approved no
Call Number Admin @ si @ CME2020 Serial 3514
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Author (down) Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix M. Martinez
Title What does it mean to learn in deep networks? And, how does one detect adversarial attacks? Type Conference Article
Year 2019 Publication 32nd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 4752-4761
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Abstract The flexibility and high-accuracy of Deep Neural Networks (DNNs) has transformed computer vision. But, the fact that we do not know when a specific DNN will work and when it will fail has resulted in a lack of trust. A clear example is self-driving cars; people are uncomfortable sitting in a car driven by algorithms that may fail under some unknown, unpredictable conditions. Interpretability and explainability approaches attempt to address this by uncovering what a DNN models, i.e., what each node (cell) in the network represents and what images are most likely to activate it. This can be used to generate, for example, adversarial attacks. But these approaches do not generally allow us to determine where a DNN will succeed or fail and why. i.e., does this learned representation generalize to unseen samples? Here, we derive a novel approach to define what it means to learn in deep networks, and how to use this knowledge to detect adversarial attacks. We show how this defines the ability of a network to generalize to unseen testing samples and, most importantly, why this is the case.
Address California; June 2019
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Area Expedition Conference CVPR
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ CME2019 Serial 3332
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Author (down) Ciprian Corneanu; Meysam Madadi; Sergio Escalera
Title Deep Structure Inference Network for Facial Action Unit Recognition Type Conference Article
Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal
Volume 11216 Issue Pages 309-324
Keywords Computer Vision; Machine Learning; Deep Learning; Facial Expression Analysis; Facial Action Units; Structure Inference
Abstract Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for general facial expression analysis. Recently, efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between AUs. We propose a deep neural architecture that tackles both problems by combining learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stages. We show that by training the model end-to-end with increased supervision we improve state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets, respectively.
Address Munich; September 2018
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
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ISSN ISBN Medium
Area Expedition Conference ECCV
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ CME2018 Serial 3205
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Author (down) Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera
Title Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History Type Journal Article
Year 2016 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 28 Issue 8 Pages 1548-1568
Keywords Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal
Abstract Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.
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Notes HuPBA;MILAB; Approved no
Call Number Admin @ si @ COC2016 Serial 2718
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Author (down) ChunYang; Xu Cheng Yin; Hong Yu; Dimosthenis Karatzas; Yu Cao
Title ICDAR2017 Robust Reading Challenge on Text Extraction from Biomedical Literature Figures (DeTEXT) Type Conference Article
Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1444-1447
Keywords
Abstract Hundreds of millions of figures are available in the biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information and understanding biomedical documents. Unlike images in the open domain, biomedical figures present a variety of unique challenges. For example, biomedical figures typically have complex layouts, small font sizes, short text, specific text, complex symbols and irregular text arrangements. This paper presents the final results of the ICDAR 2017 Competition on Text Extraction from Biomedical Literature Figures (ICDAR2017 DeTEXT Competition), which aims at extracting (detecting and recognizing) text from biomedical literature figures. Similar to text extraction from scene images and web pictures, ICDAR2017 DeTEXT Competition includes three major tasks, i.e., text detection, cropped word recognition and end-to-end text recognition. Here, we describe in detail the data set, tasks, evaluation protocols and participants of this competition, and report the performance of the participating methods.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-5386-3586-5 Medium
Area Expedition Conference ICDAR
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ YCY2017 Serial 3098
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