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Author Marçal Rusiñol
Title (up) A Model of Vectorial Signatures in Terms of Expressive Sub-Shapes: Symbol Indexation in Technical Documents Type Report
Year 2006 Publication CVC Technical Report #94 Abbreviated Journal
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Address CVC (UAB)
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Notes DAG Approved no
Call Number DAG @ dag @ Rus2006 Serial 668
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Author J. Martinez; Eva Costa; P. Herreros; F. Javier Sanchez; Ramon Baldrich
Title (up) A Modular and Scalable Architecture for PC-Based Real-Time Vision Systems Type Journal
Year 2003 Publication Real–Time Imaging, 9: 99–112 (IF: 0.512) Abbreviated Journal
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Notes CIC Approved no
Call Number CAT @ cat @ MCH2003b Serial 394
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Author Agnes Borras; Josep Llados
Title (up) A Multi-Scale Layout Descriptor Based on Delaunay Triangulation for Image Retrieval Type Conference Article
Year 2008 Publication 3rd International Conference on Computer Vision Theory and Applications VISAPP (2) 2008 Abbreviated Journal
Volume 2 Issue Pages 139-144
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Address Funchal, Madeira (Portugal)
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Notes DAG Approved no
Call Number DAG @ dag @ BoL2008 Serial 981
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Author Debora Gil; Guillermo Torres
Title (up) 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
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Address Virtual; June 2020
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Notes IAM; 600.139; 600.145 Approved no
Call Number Admin @ si @ GiT2020 Serial 3472
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Author Guillermo Torres; Debora Gil
Title (up) 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 Maria Vanrell; Jordi Vitria; Xavier Roca
Title (up) A multidimensional scaling approach to explore the behavior of a texture perception algorithm. Type Journal Article
Year 1997 Publication Machine Vision and Applications Abbreviated Journal
Volume 9 Issue Pages 262–271
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Notes OR;ISE;CIC;MV Approved no
Call Number BCNPCL @ bcnpcl @ VVR1997 Serial 35
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Author Josep Llados; Felipe Lumbreras; X. Varona
Title (up) A multidocument platform for automatic reading of identity cards. Type Miscellaneous
Year 1999 Publication Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. Abbreviated Journal
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Address Bilbao
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Notes ADAS;DAG Approved no
Call Number ADAS @ adas @ LLV1999 Serial 7
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Author Josep Brugues Pujolras; Lluis Gomez; Dimosthenis Karatzas
Title (up) A Multilingual Approach to Scene Text Visual Question Answering Type Conference Article
Year 2022 Publication Document Analysis Systems.15th IAPR International Workshop, (DAS2022) Abbreviated Journal
Volume Issue Pages 65-79
Keywords Scene text; Visual question answering; Multilingual word embeddings; Vision and language; Deep learning
Abstract Scene Text Visual Question Answering (ST-VQA) has recently emerged as a hot research topic in Computer Vision. Current ST-VQA models have a big potential for many types of applications but lack the ability to perform well on more than one language at a time due to the lack of multilingual data, as well as the use of monolingual word embeddings for training. In this work, we explore the possibility to obtain bilingual and multilingual VQA models. In that regard, we use an already established VQA model that uses monolingual word embeddings as part of its pipeline and substitute them by FastText and BPEmb multilingual word embeddings that have been aligned to English. Our experiments demonstrate that it is possible to obtain bilingual and multilingual VQA models with a minimal loss in performance in languages not used during training, as well as a multilingual model trained in multiple languages that match the performance of the respective monolingual baselines.
Address La Rochelle, France; May 22–25, 2022
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Notes DAG; 611.004; 600.155; 601.002 Approved no
Call Number Admin @ si @ BGK2022b Serial 3695
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Author Carles Fernandez; Jordi Gonzalez
Title (up) A Multilingually-Extensible Module for Natural Language Generation Type Report
Year 2008 Publication CVC Technical Report #120 Abbreviated Journal
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Address Barcelona, Spain
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Notes Approved no
Call Number ISE @ ise @ FeG2008 Serial 1146
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Author Marco Pedersoli
Title (up) A Multiresolution Cascade for Human Detection Type Miscellaneous
Year 2008 Publication CVC Technical Report #126 Abbreviated Journal
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Address Barcelona, Spain
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Notes ISE Approved no
Call Number Admin @ si @ Ped2008 Serial 1148
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Author J. Nuñez; Xavier Otazu; M.T. Merino
Title (up) A Multiresolution-Based Method for the Determination of the Relative Resolution between Images. First Application to Remote Sensing and Medical Images Type Journal
Year 2005 Publication International Journal of Imaging Systems and Technology, 15(5): 225–235 (IF: 0.439) Abbreviated Journal
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Notes CIC Approved no
Call Number CAT @ cat @ NOM2005 Serial 645
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Author Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados
Title (up) 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
Keywords
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 David Berga; Xavier Otazu
Title (up) A neurodynamic model of saliency prediction in v1 Type Journal Article
Year 2022 Publication Neural Computation Abbreviated Journal NEURALCOMPUT
Volume 34 Issue 2 Pages 378-414
Keywords
Abstract Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex.
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Notes NEUROBIT; 600.128; 600.120 Approved no
Call Number Admin @ si @ BeO2022 Serial 3696
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Author Olivier Penacchio; Xavier Otazu; Laura Dempere-Marco
Title (up) A Neurodynamical Model of Brightness Induction in V1 Type Journal Article
Year 2013 Publication PloS ONE Abbreviated Journal Plos
Volume 8 Issue 5 Pages e64086
Keywords
Abstract Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Recent neurophysiological evidence suggests that brightness information might be explicitly represented in V1, in contrast to the more common assumption that the striate cortex is an area mostly responsive to sensory information. Here we investigate possible neural mechanisms that offer a plausible explanation for such phenomenon. To this end, a neurodynamical model which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual influences is presented. The proposed computational model successfully accounts for well known psychophysical effects for static contexts and also for brightness induction in dynamic contexts defined by modulating the luminance of surrounding areas. This work suggests that intra-cortical interactions in V1 could, at least partially, explain brightness induction effects and reveals how a common general architecture may account for several different fundamental processes, such as visual saliency and brightness induction, which emerge early in the visual processing pathway.
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Notes CIC Approved no
Call Number Admin @ si @ POD2013 Serial 2242
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Author Olivier Penacchio; Laura Dempere-Marco; Xavier Otazu
Title (up) A Neurodynamical Model Of Brightness Induction In V1 Following Static And Dynamic Contextual Influences Type Abstract
Year 2012 Publication 8th Federation of European Neurosciences Abbreviated Journal
Volume 6 Issue Pages 63-64
Keywords
Abstract Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Although striate cortex is traditionally regarded as an area mostly responsive to ensory (i.e. retinal) information,
neurophysiological evidence suggests that perceived brightness information mightbe explicitly represented in V1.
Such evidence has been observed both in anesthetised cats where neuronal response modulations have been found to follow luminance changes outside the receptive felds and in human fMRI measurements. In this work, possible neural mechanisms that ofer a plausible explanation for such phenomenon are investigated. To this end, we consider the model proposed by Z.Li (Li, Network:Comput. Neural Syst., 10 (1999)) which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual infuences, i.e. layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has reproduced other phenomena such as contour detection and preattentive segmentation, which share with brightness induction the relevant efect of contextual infuences. We have extended the original model such that the input to the network is obtained from a complete multiscale and multiorientation wavelet decomposition, thereby allowing the recovery of an image refecting the perceived intensity. The proposed model successfully accounts for well known psychophysical efects for static contexts (among them: the White's and modifed White's efects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction efects) and also for brigthness induction in dynamic contexts defned by modulating the luminance of surrounding areas (e.g. the brightness of a static central area is perceived to vary in antiphase to the sinusoidal luminance changes of its surroundings). This work thus suggests that intra-cortical interactions in V1 could partially explain perceptual brightness induction efects and reveals how a common general architecture may account for several different fundamental processes emerging early in the visual processing pathway.
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Notes CIC Approved no
Call Number Admin @ si @ PDO2012b Serial 2181
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