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Author (up) Carlos David Martinez Hinarejos; Josep Llados; Alicia Fornes; Francisco Casacuberta; Lluis de Las Heras; Joan Mas; Moises Pastor; Oriol Ramos Terrades; Joan Andreu Sanchez; Enrique Vidal; Fernando Vilariño
Title Context, multimodality, and user collaboration in handwritten text processing: the CoMUN-HaT project Type Conference Article
Year 2016 Publication 3rd IberSPEECH Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Processing of handwritten documents is a task that is of wide interest for many
purposes, such as those related to preserve cultural heritage. Handwritten text recognition techniques have been successfully applied during the last decade to obtain transcriptions of handwritten documents, and keyword spotting techniques have been applied for searching specific terms in image collections of handwritten documents. However, results on transcription and indexing are far from perfect. In this framework, the use of new data sources arises as a new paradigm that will allow for a better transcription and indexing of handwritten documents. Three main different data sources could be considered: context of the document (style, writer, historical time, topics,. . . ), multimodal data (representations of the document in a different modality, such as the speech signal of the dictation of the text), and user feedback (corrections, amendments,. . . ). The CoMUN-HaT project aims at the integration of these different data sources into the transcription and indexing task for handwritten documents: the use of context derived from the analysis of the documents, how multimodality can aid the recognition process to obtain more accurate transcriptions (including transcription in a modern version of the language), and integration into a userin-the-loop assisted text transcription framework. This will be reflected in the construction of a transcription and indexing platform that can be used by both professional and nonprofessional users, contributing to crowd-sourcing activities to preserve cultural heritage and to obtain an accessible version of the involved corpus.
Address Lisboa; Portugal; November 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 IberSPEECH
Notes DAG; MV; 600.097;SIAI Approved no
Call Number Admin @ si @MLF2016 Serial 2813
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Author (up) Carlos Martin Isla; Victor M Campello; Cristian Izquierdo; Kaisar Kushibar; Carla Sendra Balcells; Polyxeni Gkontra; Alireza Sojoudi; Mitchell J Fulton; Tewodros Weldebirhan Arega; Kumaradevan Punithakumar; Lei Li; Xiaowu Sun; Yasmina Al Khalil; Di Liu; Sana Jabbar; Sandro Queiros; Francesco Galati; Moona Mazher; Zheyao Gao; Marcel Beetz; Lennart Tautz; Christoforos Galazis; Marta Varela; Markus Hullebrand; Vicente Grau; Xiahai Zhuang; Domenec Puig; Maria A Zuluaga; Hassan Mohy Ud Din; Dimitris Metaxas; Marcel Breeuwer; Rob J van der Geest; Michelle Noga; Stephanie Bricq; Mark E Rentschler; Andrea Guala; Steffen E Petersen; Sergio Escalera; Jose F Rodriguez Palomares; Karim Lekadir
Title Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&ms Challenge Type Journal Article
Year 2023 Publication IEEE Journal of Biomedical and Health Informatics Abbreviated Journal JBHI
Volume 27 Issue 7 Pages 3302-3313
Keywords
Abstract In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.
Address
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Notes HUPBA Approved no
Call Number Admin @ si @ MCI2023 Serial 3880
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Author (up) Carlos Martin-Isla; Maryam Asadi-Aghbolaghi; Polyxeni Gkontra; Victor M. Campello; Sergio Escalera; Karim Lekadir
Title Stacked BCDU-net with semantic CMR synthesis: application to Myocardial Pathology Segmentation challenge Type Conference Article
Year 2020 Publication MYOPS challenge and workshop Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Virtual; October 2020
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 MICCAIW
Notes HUPBA Approved no
Call Number Admin @ si @ MAG2020 Serial 3518
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Author (up) Carme Julia
Title Missig Data Matrix Factorization Addressing the Structure from Motion Problem Type Miscellaneous
Year 2008 Publication CVC–UAB Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Bellaterra
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 978–84–935251–6–3 Medium
Area Expedition Conference
Notes Approved no
Call Number Admin @ si @ Jul2008 Serial 1104
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Author (up) Carme Julia
Title Motion segmentation through factorization. Application to night driving assistance Type Report
Year 2004 Publication CVC Technical Report #79 Abbreviated Journal
Volume Issue Pages
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Abstract
Address CVC (UAB)
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ISSN ISBN Medium
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Notes Approved no
Call Number Admin @ si @ 38169 Serial 506
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras
Title Aprendiendo a recrear la realidad en 3D Type Journal
Year 2008 Publication UAB Divulga, Revista de divulgacion cientifica Abbreviated Journal
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Abstract
Address
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Area Expedition Conference
Notes spreading;ADAS Approved no
Call Number ADAS @ adas @ JSL2008b Serial 1472
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Antonio Lopez
Title Recovery of Surface Normals and Reflectance from Different Lighting Conditions Type Conference Article
Year 2008 Publication 5th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 5112 Issue Pages 315–325
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Abstract
Address
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2008c Serial 1014
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat
Title Photometric Stereo through and Adapted Alternation Approach Type Conference Article
Year 2008 Publication IEEE International Conference on Image Processing, Abbreviated Journal
Volume Issue Pages 1500–1503
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Abstract
Address San Diego; CA; USA
Corporate Author Thesis
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2008d Serial 1016
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Rank Estimation in Missing Data Matrix Problems Type Journal Article
Year 2011 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV
Volume 39 Issue 2 Pages 140-160
Keywords
Abstract A novel technique for missing data matrix rank estimation is presented. It is focused on matrices of trajectories, where every element of the matrix corresponds to an image coordinate from a feature point of a rigid moving object at a given frame; missing data are represented as empty entries. The objective of the proposed approach is to estimate the rank of a missing data matrix in order to fill in empty entries with some matrix completion method, without using or assuming neither the number of objects contained in the scene nor the kind of their motion. The key point of the proposed technique consists in studying the frequency behaviour of the individual trajectories, which are seen as 1D signals. The main assumption is that due to the rigidity of the moving objects, the frequency content of the trajectories will be similar after filling in their missing entries. The proposed rank estimation approach can be used in different computer vision problems, where the rank of a missing data matrix needs to be estimated. Experimental results with synthetic and real data are provided in order to empirically show the good performance of the proposed approach.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0924-9907 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ JSL2011; Serial 1710
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title An Iterative Multiresolution Scheme for SFM with Missing Data: single and multiple object scenes Type Journal Article
Year 2010 Publication Image and Vision Computing Abbreviated Journal IMAVIS
Volume 28 Issue 1 Pages 164-176
Keywords
Abstract Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach.
Address
Corporate Author Thesis
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0262-8856 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2010 Serial 1278
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach Type Journal Article
Year 2009 Publication International Journal of Electronic Commerce Abbreviated Journal
Volume 14 Issue 1 Pages 89-108
Keywords
Abstract The paper presents a factorization-based approach to make predictions in recommender systems. These systems are widely used in electronic commerce to help customers find products according to their preferences. Taking into account the customer's ratings of some products available in the system, the recommender system tries to predict the ratings the customer would give to other products in the system. The proposed factorization-based approach uses all the information provided to compute the predicted ratings, in the same way as approaches based on Singular Value Decomposition (SVD). The main advantage of this technique versus SVD-based approaches is that it can deal with missing data. It also has a smaller computational cost. Experimental results with public data sets are provided to show that the proposed adapted factorization approach gives better predicted ratings than a widely used SVD-based approach.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1086-4415 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2009b Serial 1237
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title An iterative multiresolution scheme for SFM with missing data Type Journal Article
Year 2009 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV
Volume 34 Issue 3 Pages 240–258
Keywords
Abstract Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach.
Address
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2009a Serial 1163
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Rank Estimation in 3D Multibody Motion Segmentation Type Journal Article
Year 2008 Publication Electronic Letters Abbreviated Journal
Volume 44 Issue 4 Pages 279-280
Keywords
Abstract A novel technique for rank estimation in 3D multibody motion segmentation is proposed. It is based on the study of the frequency spectra of moving rigid objects and does not use or assume a prior knowledge of the objects contained in the scene (i.e. number of objects and motion). The significance of rank estimation on multibody motion segmentation results is shown by using two motion segmentation algorithms over both synthetic and real data.
Address
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2008a Serial 939
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title An Adapted Alternation Approach for Recommender Systems Type Conference Article
Year 2008 Publication IEEE International Conference on e–Business Engineering, Abbreviated Journal
Volume Issue Pages 128–135
Keywords
Abstract This paper presents an adaptation of the alternation technique to tackle the prediction task in recommender systems. These systems are widely considered in electronic commerce to help customers to find products they will probably like or dislike. As the SVD-based approaches, the proposed adapted alternation technique uses all the information stored in the system to find the predictions. The main advantage of this technique with respect to the SVD-based ones is that it can deal with missing data. Furthermore, it has a smaller computational cost. Experimental results with public data sets are provided in order to show the viability of the proposed adapted alternation approach.
Address Xi’an (Xina)
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2008e Serial 1044
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Motion Segmentation from Feature Trajectories with Missing Data Type Conference Article
Year 2007 Publication 3rd. Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal IbPRIA 2007
Volume LNCS 4477 Issue Pages 483–490
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Abstract
Address Girona (Spain)
Corporate Author Thesis
Publisher Place of Publication Editor J. Marti et al. (Eds.)
Language Summary Language Original Title
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ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2007a Serial 814
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