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Author (up) M. Gomez; J. Mauri; E. Fernandez-Nofrerias; Oriol Rodriguez-Leor; Carme Julia; Petia Radeva; David Rotger; V. Valle edit  openurl
  Title Nuevos Avances para la correlacion de imagenes angiograficas y de ecograia intracoronaria. Type Miscellaneous
  Year 2002 Publication Congreso de las Enfermedades Cardiovasculares, XXXVIII Congreso de la Sociedad Española de Cardiologia. Abbreviated Journal  
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  Address Madrid, Espanya  
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  Area Expedition Conference  
  Notes MILAB Approved no  
  Call Number BCNPCL @ bcnpcl @ GMF2002c Serial 309  
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Author (up) M. Gomez; J. Mauri; Eduard Fernandez-Nofrerias; Oriol Rodriguez-Leor; Carme Julia; Debora Gil; Petia Radeva edit  openurl
  Title Reconstrucción de un modelo espacio-temporal de la luz del vaso a partir de secuencias de ecografía intracoronaria Type Conference Article
  Year 2002 Publication XXXVIII Congreso Nacional de la Sociedad Española de Cardiología. Abbreviated Journal  
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  Notes IAM;ADAS;MILAB Approved no  
  Call Number IAM @ iam @ GMF2002d Serial 1516  
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Author (up) M. Gonzalez-Audicana; Xavier Otazu; O. Fors; A. Seco edit  url
openurl 
  Title Comparison between Mallats and the trous discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images Type Journal
  Year 2005 Publication International Journal of Remote Sensing, 26(3):595–614 (IF: 0.925) Abbreviated Journal  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ GOF2005 Serial 530  
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Author (up) M. Gonzalez-Audicana; Xavier Otazu; O. Fors; R Garcia; J. Nuñez edit  openurl
  Title Fusion of different spatial and spectral resolution images: development, apllication and comparison of new methods based on wavelets. Type Miscellaneous
  Year 2002 Publication Proceedings of the 1st. International Symposium Recent Advances in Quantitative Remote Sensing. Abbreviated Journal  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ GOF2002 Serial 291  
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Author (up) M. Ivasic-Kos; M. Pobar; Jordi Gonzalez edit   pdf
doi  openurl
  Title Active Player Detection in Handball Videos Using Optical Flow and STIPs Based Measures Type Conference Article
  Year 2019 Publication 13th International Conference on Signal Processing and Communication Systems Abbreviated Journal  
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  Abstract In handball videos recorded during the training, multiple players are present in the scene at the same time. Although they all might move and interact, not all players contribute to the currently relevant exercise nor practice the given handball techniques. The goal of this experiment is to automatically determine players on training footage that perform given handball techniques and are therefore considered active. It is a very challenging task for which a precise object detector is needed that can handle cluttered scenes with poor illumination, with many players present in different sizes and distances from the camera, partially occluded, moving fast. To determine which of the detected players are active, additional information is needed about the level of player activity. Since many handball actions are characterized by considerable changes in speed, position, and variations in the player's appearance, we propose using spatio-temporal interest points (STIPs) and optical flow (OF). Therefore, we propose an active player detection method combining the YOLO object detector and two activity measures based on STIPs and OF. The performance of the proposed method and activity measures are evaluated on a custom handball video dataset acquired during handball training lessons.  
  Address Gold Coast; Australia; December 2019  
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  Area Expedition Conference ICSPCS2  
  Notes ISE; 600.098; 600.119 Approved no  
  Call Number Admin @ si @ IPG2019 Serial 3415  
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Author (up) M. Li; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu edit   pdf
openurl 
  Title Learning to Rank for Active Learning: A Listwise Approach Type Conference Article
  Year 2020 Publication 25th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 5587-5594  
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  Abstract Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.). The goal of active learning is to automatically select a number of unlabeled samples for annotation (according to a budget), based on an acquisition function, which indicates how valuable a sample is for training the model. The learning loss method is a task-agnostic approach which attaches a module to learn to predict the target loss of unlabeled data, and select data with the highest loss for labeling. In this work, we follow this strategy but we define the acquisition function as a learning to rank problem and rethink the structure of the loss prediction module, using a simple but effective listwise approach. Experimental results on four datasets demonstrate that our method outperforms recent state-of-the-art active learning approaches for both image classification and regression tasks.  
  Address Virtual; January 2021  
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  Area Expedition Conference ICPR  
  Notes LAMP; 600.120 Approved no  
  Call Number Admin @ si @ LLW2020a Serial 3511  
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Author (up) M. Navarro edit  openurl
  Title Reconeixement d´objectes amb metodes basats en color: avaluacio en un entorn poc controlat Type Report
  Year 1999 Publication CVC Technical Report #28 Abbreviated Journal  
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  Address CVC (UAB)  
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  Notes Approved no  
  Call Number Admin @ si @ Nav1999 Serial 190  
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Author (up) M. Pros edit  openurl
  Title Indexacio icònica amb 2D-String per al reconoixement de persones segons la seva vestimenta Type Report
  Year 2000 Publication CVC Technical Report #44 Abbreviated Journal  
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  Address CVC (UAB)  
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  Notes Approved no  
  Call Number Admin @ si @ Pro2000 Serial 345  
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Author (up) M. Visani; Oriol Ramos Terrades; Salvatore Tabbone edit  doi
openurl 
  Title A Protocol to Characterize the Descriptive Power and the Complementarity of Shape Descriptors Type Journal Article
  Year 2011 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 14 Issue 1 Pages 87-100  
  Keywords Document analysis; Shape descriptors; Symbol description; Performance characterization; Complementarity analysis  
  Abstract Most document analysis applications rely on the extraction of shape descriptors, which may be grouped into different categories, each category having its own advantages and drawbacks (O.R. Terrades et al. in Proceedings of ICDAR’07, pp. 227–231, 2007). In order to improve the richness of their description, many authors choose to combine multiple descriptors. Yet, most of the authors who propose a new descriptor content themselves with comparing its performance to the performance of a set of single state-of-the-art descriptors in a specific applicative context (e.g. symbol recognition, symbol spotting...). This results in a proliferation of the shape descriptors proposed in the literature. In this article, we propose an innovative protocol, the originality of which is to be as independent of the final application as possible and which relies on new quantitative and qualitative measures. We introduce two types of measures: while the measures of the first type are intended to characterize the descriptive power (in terms of uniqueness, distinctiveness and robustness towards noise) of a descriptor, the second type of measures characterizes the complementarity between multiple descriptors. Characterizing upstream the complementarity of shape descriptors is an alternative to the usual approach where the descriptors to be combined are selected by trial and error, considering the performance characteristics of the overall system. To illustrate the contribution of this protocol, we performed experimental studies using a set of descriptors and a set of symbols which are widely used by the community namely ART and SC descriptors and the GREC 2003 database.  
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  Notes DAG; IF 1.091 Approved no  
  Call Number Admin @ si @VRT2011 Serial 1856  
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Author (up) M. Visani; V.C.Kieu; Alicia Fornes; N.Journet edit   pdf
doi  openurl
  Title The ICDAR 2013 Music Scores Competition: Staff Removal Type Conference Article
  Year 2013 Publication 12th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 1439-1443  
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  Abstract The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results.  
  Address Washington; USA; August 2013  
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  ISSN 1520-5363 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.045; 600.061 Approved no  
  Call Number Admin @ si @ VKF2013 Serial 2338  
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Author (up) M.A. Garcia; Angel Sappa edit  openurl
  Title Efficient Generation of Discontinuity-Preserving Adaptive Triangulations from Range Images Type Journal
  Year 2004 Publication IEEE Trans. on Systems, Man, and Cybernetics (Part B), 34(5):2003–2014 (IF: 1.052) Abbreviated Journal  
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  Notes Approved no  
  Call Number ADAS @ adas @ GaS2004 Serial 457  
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Author (up) M.J. Yzuel; J. Pladellorens; Joan Serrat; A. Dupuy edit  openurl
  Title Application restauration and edge detection techniques in the calculation of left ventricular volumes. Type Conference Article
  Year 1993 Publication Optics in Medicine, Biology and Environmental Research : Selected contributions to the first International Conference on Optics within Life Sciences (OWLS I) Abbreviated Journal  
  Volume Issue Pages 374-375  
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  Publisher Elsevier Place of Publication Editor  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ YPS1993 Serial 244  
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Author (up) Maciej Wielgosz; Antonio Lopez; Muhamad Naveed Riaz edit   pdf
url  openurl
  Title CARLA-BSP: a simulated dataset with pedestrians Type Miscellaneous
  Year 2023 Publication Arxiv Abbreviated Journal  
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  Abstract We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestrian detection, autoencoding, pose estimation, and pose lifting. We also showcase baseline results.  
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  Notes ADAS Approved no  
  Call Number Admin @ si @ WLN2023 Serial 3866  
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Author (up) Maedeh Aghaei; Mariella Dimiccoli; C. Canton-Ferrer; Petia Radeva edit   pdf
url  doi
openurl 
  Title Towards social pattern characterization from egocentric photo-streams Type Journal Article
  Year 2018 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume 171 Issue Pages 104-117  
  Keywords Social pattern characterization; Social signal extraction; Lifelogging; Convolutional and recurrent neural networks  
  Abstract Following the increasingly popular trend of social interaction analysis in egocentric vision, this article presents a comprehensive pipeline for automatic social pattern characterization of a wearable photo-camera user. The proposed framework relies merely on the visual analysis of egocentric photo-streams and consists of three major steps. The first step is to detect social interactions of the user where the impact of several social signals on the task is explored. The detected social events are inspected in the second step for categorization into different social meetings. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task; finally, LSTM is employed to classify the time-series. The last step of the framework is to characterize social patterns of the user. Our goal is to quantify the duration, the diversity and the frequency of the user social relations in various social situations. This goal is achieved by the discovery of recurrences of the same people across the whole set of social events related to the user. Experimental evaluation over EgoSocialStyle – the proposed dataset in this work, and EGO-GROUP demonstrates promising results on the task of social pattern characterization from egocentric photo-streams.  
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  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ ADC2018 Serial 3022  
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Author (up) Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva edit  doi
openurl 
  Title Towards social interaction detection in egocentric photo-streams Type Conference Article
  Year 2015 Publication Proceedings of SPIE, 8th International Conference on Machine Vision , ICMV 2015 Abbreviated Journal  
  Volume 9875 Issue Pages  
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  Abstract Detecting social interaction in videos relying solely on visual cues is a valuable task that is receiving increasing attention in recent years. In this work, we address this problem in the challenging domain of egocentric photo-streams captured by a low temporal resolution wearable camera (2fpm). The major difficulties to be handled in this context are the sparsity of observations as well as unpredictability of camera motion and attention orientation due to the fact that the camera is worn as part of clothing. Our method consists of four steps: multi-faces localization and tracking, 3D localization, pose estimation and analysis of f-formations. By estimating pair-to-pair interaction probabilities over the sequence, our method states the presence or absence of interaction with the camera wearer and specifies which people are more involved in the interaction. We tested our method over a dataset of 18.000 images and we show its reliability on our considered purpose. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.  
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  Area Expedition Conference ICMV  
  Notes MILAB Approved no  
  Call Number Admin @ si @ ADR2015a Serial 2702  
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