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Craig Von Land; Ricardo Toledo; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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TeleRegions: Application of Telematics in Cardiac Care. |
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Miscellaneous |
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1997 |
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Computers in Cardiology, 1997. Piscataway, NJ: IEEE Computer Society Press, 24: 645–8. |
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ISE @ ise @ VTV1997 |
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64 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Craig Von Land; Ricardo Toledo; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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CARE: Computer Assisted Radiology Environment |
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Miscellaneous |
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1996 |
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Tecnologia de Imagenes Medicas, Convencion Iberoamericana sobre la Salud en la Sociedad Global de la Informacion. |
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Buenos Aires, Argentina |
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ISE @ ise @ VTV1996a |
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99 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Craig Von Land; Ricardo Toledo; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
TeleRegion: Tele-Applications for European Regions |
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Miscellaneous |
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1996 |
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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. |
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Buenos Aires, Argentina |
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ISE @ ise @ VTV1996b |
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101 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Craig Von Land; Ricardo Toledo; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Object Oriented Design of the DICOM standard |
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Miscellaneous |
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1996 |
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International Symposium on Cardiovascular Imaging. |
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Leiden, The Netherlands |
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ISE @ ise @ VTV1996c |
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104 |
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Corina Krauter; Ursula Reiter; Albrecht Schmidt; Marc Masana; Rudolf Stollberger; Michael Fuchsjager; Gert Reiter |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Objective extraction of the temporal evolution of the mitral valve vortex ring from 4D flow MRI |
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2019 |
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27th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine |
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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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ISMRM |
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LAMP; 600.120 |
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Admin @ si @ KRS2019 |
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3300 |
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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 |
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Title |
DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification |
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Journal Article |
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2021 |
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BMC Bioinformatics |
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22 |
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473 |
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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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HUPBA; no proj |
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Admin @ si @ DAP2021 |
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3650 |
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Clement Guerin; Christophe Rigaud; Karell Bertet; Jean-Christophe Burie; Arnaud Revel ; Jean-Marc Ogier |
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Title |
Réduction de l’espace de recherche pour les personnages de bandes dessinées |
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Conference Article |
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2014 |
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19th National Congress Reconnaissance de Formes et l'Intelligence Artificielle |
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contextual search; document analysis; comics characters |
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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%. |
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Rouen; Francia; July 2014 |
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RFIA |
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DAG; 600.077 |
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no |
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Admin @ si @ GRB2014 |
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2480 |
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Claudio Baecchi; Francesco Turchini; Lorenzo Seidenari; Andrew Bagdanov; Alberto del Bimbo |
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Title |
Fisher vectors over random density forest for object recognition |
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Conference Article |
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2014 |
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22nd International Conference on Pattern Recognition |
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4328-4333 |
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Stockholm; Sweden; August 2014 |
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ICPR |
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LAMP; 600.079 |
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Admin @ si @ BTS2014 |
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2518 |
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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 |
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Title |
Emotional Features of Interactions With Empathic Agents |
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Conference Article |
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2021 |
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IEEE/CVF International Conference on Computer Vision Workshops |
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2168-2176 |
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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. |
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VIRTUAL; October 2021 |
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ICCVW |
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HUPBA; no proj |
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no |
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Admin @ si @ GBB2021 |
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3647 |
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Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Computing the Testing Error Without a Testing Set |
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2020 |
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33rd IEEE Conference on Computer Vision and Pattern Recognition |
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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. |
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Virtual CVPR |
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HuPBA; no proj |
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Admin @ si @ CEM2020 |
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3437 |
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Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Explainable Early Stopping for Action Unit Recognition |
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Conference Article |
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2020 |
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Faces and Gestures in E-health and welfare workshop |
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693-699 |
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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. |
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Virtual; November 2020 |
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FGW |
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HUPBA; |
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no |
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Admin @ si @ CME2020 |
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3514 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix M. Martinez |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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What does it mean to learn in deep networks? And, how does one detect adversarial attacks? |
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2019 |
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32nd IEEE Conference on Computer Vision and Pattern Recognition |
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4752-4761 |
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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. |
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California; June 2019 |
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CVPR |
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HuPBA; no proj |
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no |
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Admin @ si @ CME2019 |
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3332 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Deep Structure Inference Network for Facial Action Unit Recognition |
Type |
Conference Article |
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2018 |
Publication |
15th European Conference on Computer Vision |
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11216 |
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309-324 |
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Computer Vision; Machine Learning; Deep Learning; Facial Expression Analysis; Facial Action Units; Structure Inference |
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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. |
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Munich; September 2018 |
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HUPBA; no proj |
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Admin @ si @ CME2018 |
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3205 |
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Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera |
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Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History |
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Journal Article |
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2016 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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28 |
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8 |
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1548-1568 |
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Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal |
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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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HuPBA;MILAB; |
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Admin @ si @ COC2016 |
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2718 |
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ChunYang; Xu Cheng Yin; Hong Yu; Dimosthenis Karatzas; Yu Cao |
![goto web page (via DOI) doi](img/doi.gif)
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ICDAR2017 Robust Reading Challenge on Text Extraction from Biomedical Literature Figures (DeTEXT) |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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1444-1447 |
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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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978-1-5386-3586-5 |
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ICDAR |
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DAG; 600.121 |
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no |
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Admin @ si @ YCY2017 |
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3098 |
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