Home [21–30] << 31 32 33 34 35 36 37 38 39 40 >> [41–50]
List View
 | 
Citations
 | 
Details
   web
Records
Author Sonia Baeza; R.Domingo; M.Salcedo; G.Moragas; J.Deportos; I.Garcia Olive; Carles Sanchez; Debora Gil; Antoni Rosell
Title Artificial Intelligence to Optimize Pulmonary Embolism Diagnosis During Covid-19 Pandemic by Perfusion SPECT/CT, a Pilot Study Type Journal Article
Year 2021 Publication American Journal of Respiratory and Critical Care Medicine Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
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 Medium
Area Expedition Conference
Notes IAM; 600.145 Approved no
Call Number (up) Admin @ si @ BDS2021 Serial 3591
Permanent link to this record
 
 
Author Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva
Title Compact Evolutive Design of Error-Correcting Output Codes. Supervised and Unsupervised Ensemble Methods and Applications Type Conference Article
Year 2010 Publication European Conference on Machine Learning Abbreviated Journal
Volume I Issue Pages 119-128
Keywords
Abstract
Address
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 Medium
Area Expedition Conference ECML
Notes MILAB; OR;HUPBA;MV Approved no
Call Number (up) Admin @ si @ BEB2010 Serial 1775
Permanent link to this record
 
 
Author Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol
Title Minimal Design of Error-Correcting Output Codes Type Journal Article
Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 33 Issue 6 Pages 693-702
Keywords Multi-class classification; Error-correcting output codes; Ensemble of classifiers
Abstract IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes MILAB; OR;HuPBA;MV Approved no
Call Number (up) Admin @ si @ BEB2011a Serial 1800
Permanent link to this record
 
 
Author Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva
Title On the Design of Low Redundancy Error-Correcting Output Codes Type Book Chapter
Year 2011 Publication Ensembles in Machine Learning Applications Abbreviated Journal
Volume 373 Issue 2 Pages 21-38
Keywords
Abstract The classification of large number of object categories is a challenging trend in the Pattern Recognition field. In the literature, this is often addressed using an ensemble of classifiers . In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for combining classifiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a compact design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best compact ECOC code configuration. The results over several public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1860-949X ISBN 978-3-642-22909-1 Medium
Area Expedition Conference
Notes MILAB; OR;HuPBA;MV Approved no
Call Number (up) Admin @ si @ BEB2011b Serial 1886
Permanent link to this record
 
 
Author Xavier Baro; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Lukasz Romaszko; Lisheng Sun; Sebastien Treguer; Evelyne Viegas
Title Coompetitions in machine learning: case studies Type Conference Article
Year 2016 Publication 30th Annual Conference on Neural Information Processing Systems Worshops Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona; Spain; December 2016
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 Medium
Area Expedition Conference NIPSW
Notes HuPBA Approved no
Call Number (up) Admin @ si @ BEG2016 Serial 2911
Permanent link to this record
 
 
Author Parichehr Behjati Ardakani
Title Towards Efficient and Robust Convolutional Neural Networks for Single Image Super-Resolution Type Book Whole
Year 2022 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Single image super-resolution (SISR) is an important task in image processing which aims to enhance the resolution of imaging systems. Recently, SISR has witnessed great strides with the rapid development of deep learning. Recent advances in SISR are mostly devoted to designing deeper and wider networks to enhance their representation learning capacity. However, as the depth of networks increases, deep learning-based methods are faced with the challenge of computational complexity in practice. Moreover, most existing methods rarely leverage the intermediate features and also do not discriminate the computation of features by their frequencial components, thereby achieving relatively low performance. Aside from the aforementioned problems, another desired ability is to upsample images to arbitrary scales using a single model. Most current SISR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. In this thesis, we address the aforementioned issues and propose solutions to them: i) We present a novel frequency-based enhancement block which treats different frequencies in a heterogeneous way and also models inter-channel dependencies, which consequently enrich the output feature. Thus it helps the network generate more discriminative representations by explicitly recovering finer details. ii) We introduce OverNet which contains two main parts: a lightweight feature extractor that follows a novel recursive framework of skip and dense connections to reduce low-level feature degradation, and an overscaling module that generates an accurate SR image by internally constructing an overscaled intermediate representation of the output features. Then, to solve the problem of reconstruction at arbitrary scale factors, we introduce a novel multi-scale loss, that allows the simultaneous training of all scale factors using a single model. iii) We propose a directional variance attention network which leverages a novel attention mechanism to enhance features in different channels and spatial regions. Moreover, we introduce a novel procedure for using attention mechanisms together with residual blocks to facilitate the preservation of finer details. Finally, we demonstrate that our approaches achieve considerably better performance than previous state-of-the-art methods, in terms of both quantitative and visual quality.
Address April, 2022
Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor Jordi Gonzalez;Xavier Roca;Pau Rodriguez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-124793-1-7 Medium
Area Expedition Conference
Notes ISE Approved no
Call Number (up) Admin @ si @ Beh2022 Serial 3713
Permanent link to this record
 
 
Author Shida Beigpour
Title Physics-based Reflectance Estimation Applied to Recoloring Type Report
Year 2009 Publication CVC Technical Report Abbreviated Journal
Volume 137 Issue Pages
Keywords
Abstract
Address
Corporate Author Computer Vision Center Thesis Master's thesis
Publisher Place of Publication Bellaterra, Barcelona Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number (up) Admin @ si @ Bei2009 Serial 2396
Permanent link to this record
 
 
Author Shida Beigpour
Title Illumination and object reflectance modeling Type Book Whole
Year 2013 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract More realistic and accurate models of the scene illumination and object reflectance can greatly improve the quality of many computer vision and computer graphics tasks. Using such model, a more profound knowledge about the interaction of light with object surfaces can be established which proves crucial to a variety of computer vision applications. In the current work, we investigate the various existing approaches to illumination and reflectance modeling and form an analysis on their shortcomings in capturing the complexity of real-world scenes. Based on this analysis we propose improvements to different aspects of reflectance and illumination estimation in order to more realistically model the real-world scenes in the presence of complex lighting phenomena (i.e, multiple illuminants, interreflections and shadows). Moreover, we captured our own multi-illuminant dataset which consists of complex scenes and illumination conditions both outdoor and in laboratory conditions. In addition we investigate the use of synthetic data to facilitate the construction of datasets and improve the process of obtaining ground-truth information.
Address Barcelona
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Joost Van de Weijer;Ernest Valveny
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number (up) Admin @ si @ Bei2013 Serial 2267
Permanent link to this record
 
 
Author David Berga; Xavier Otazu
Title Computations of inhibition of return mechanisms by modulating V1 dynamics Type Conference Article
Year 2019 Publication 28th Annual Computational Neuroscience Meeting Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this study we present a unifed model of the visual cortex for predicting visual attention using real image scenes. Feedforward mechanisms from RGC and LGN have been functionally modeled using wavelet filters at distinct orientations and scales for each chromatic pathway (Magno-, Parvo-, Konio-cellular) and polarity (ON-/OFF-center), by processing image components in the CIE Lab space. In V1, we process cortical interactions with an excitatory-inhibitory network of fring rate neurons, initially proposed by (Li, 1999), later extended by (Penacchio et al. 2013). Firing rates from model’s output have been used as predictors of neuronal activity to be projected in a map in superior colliculus (with WTA-like computations), determining locations of visual fxations. These locations will be considered as already visited areas for future saccades, therefore we integrated a spatiotemporal function of inhibition of return mechanisms (where LIP/FEF is responsible) to feed to the model with spatial memory for next saccades. Foveation mechanisms have been simulated with a cortical magnifcation function, which distort spatial viewing properties for each fxation. Results show lower prediction errors than with respect no IoR cases (Fig. 1), and it is functionally consistent with human psychophysical measurements. Our model follows a biologically-constrained architecture, previously shown to reproduce visual saliency (Berga & Otazu, 2018), visual discomfort (Penacchio et al. 2016), brightness (Penacchio et al. 2013) and chromatic induction (Cerda & Otazu, 2016).
Address Barcelona; July 2019
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 Medium
Area Expedition Conference CNS
Notes NEUROBIT; no menciona Approved no
Call Number (up) Admin @ si @ BeO2019a Serial 3373
Permanent link to this record
 
 
Author David Berga; Xavier Otazu
Title Computational modelingof visual attention: What do we know from physiology and psychophysics? Type Conference Article
Year 2019 Publication 8th Iberian Conference on Perception Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Latest computer vision architectures use a chain of feedforward computations, mainly optimizing artificial neural networks for very specific tasks. Although their impressive performance (i.e. in saliency) using real image datasets, these models do not follow several biological principles of the human visual system (e.g. feedback and horizontal connections in cortex) and are unable to predict several visual tasks simultaneously. In this study we present biologically plausible computations from the early stages of the human visual system (i.e. retina and lateral geniculate nucleus) and lateral connections in V1. Despite the simplicity of these processes and without any type of training or optimization, simulations of firing-rate dynamics of V1 are able to predict bottom-up visual attention at distinct contexts (shown previously as well to predict visual discomfort, brightness and chromatic induction). We also show functional top-down selection mechanisms as feedback inhibition projections (i.e. prefrontal cortex for search/task-based attention and parietal area for inhibition of return). Distinct saliency model predictions are tested with eye tracking datasets in free-viewing and visual search tasks, using real images and synthetically-generated patterns. Results on predicting saliency and scanpaths show that artificial models do not outperform biologically-inspired ones (specifically for datasets that lack of common endogenous biases found in eye tracking experimentation), as well as, do not correctly predict contrast sensitivities in pop-out stimulus patterns. This work remarks the importance of considering biological principles of the visual system for building models that reproduce this (and any other) visual effects.
Address San Lorenzo El Escorial; July 2019
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 Medium
Area Expedition Conference CIP
Notes NEUROBIT; no menciona Approved no
Call Number (up) Admin @ si @ BeO2019b Serial 3374
Permanent link to this record
 
 
Author David Berga; Xavier Otazu
Title Computations of top-down attention by modulating V1 dynamics Type Conference Article
Year 2020 Publication Computational and Mathematical Models in Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address St. Pete Beach; Florida; May 2020
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 Medium
Area Expedition Conference MODVIS
Notes NEUROBIT Approved no
Call Number (up) Admin @ si @ BeO2020a Serial 3376
Permanent link to this record
 
 
Author David Berga; Xavier Otazu
Title Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1 Type Journal Article
Year 2020 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume 417 Issue Pages 270-289
Keywords
Abstract Previous studies suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return, oculomotor and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks. Results show that our model outpeforms other biologically inspired models of saliency prediction while predicting visual saccade sequences with the same model. We also show how temporal and spatial characteristics of saccade amplitude and inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts.
Address
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 Medium
Area Expedition Conference
Notes NEUROBIT Approved no
Call Number (up) Admin @ si @ BeO2020c Serial 3444
Permanent link to this record
 
 
Author David Berga; Xavier Otazu
Title 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.
Address
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 Medium
Area Expedition Conference
Notes NEUROBIT; 600.128; 600.120 Approved no
Call Number (up) Admin @ si @ BeO2022 Serial 3696
Permanent link to this record
 
 
Author Miguel Angel Bautista; Sergio Escalera; Oriol Pujol
Title On the Design of an ECOC-Compliant Genetic Algorithm Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 2 Pages 865-884
Keywords
Abstract Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches.
Address
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 Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number (up) Admin @ si @ BEP2013 Serial 2254
Permanent link to this record
 
 
Author German Barquero; Sergio Escalera; Cristina Palmero
Title BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction Type Conference Article
Year 2023 Publication IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Abbreviated Journal
Volume Issue Pages 2317-2327
Keywords
Abstract Stochastic human motion prediction (HMP) has generally been tackled with generative adversarial networks and variational autoencoders. Most prior works aim at predicting highly diverse movements in terms of the skeleton joints’ dispersion. This has led to methods predicting fast and motion-divergent movements, which are often unrealistic and incoherent with past motion. Such methods also neglect contexts that need to anticipate diverse low-range behaviors, or actions, with subtle joint displacements. To address these issues, we present BeLFusion, a model that, for the first time, leverages latent diffusion models in HMP to sample from a latent space where behavior is disentangled from pose and motion. As a result, diversity is encouraged from a behavioral perspective. Thanks to our behavior
coupler’s ability to transfer sampled behavior to ongoing motion, BeLFusion’s predictions display a variety of behaviors that are significantly more realistic than the state of the art. To support it, we introduce two metrics, the Area of
the Cumulative Motion Distribution, and the Average Pairwise Distance Error, which are correlated to our definition of realism according to a qualitative study with 126 participants. Finally, we prove BeLFusion’s generalization power in a new cross-dataset scenario for stochastic HMP.
Address 2-6 October 2023. Paris (France)
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 Medium
Area Expedition Conference ICCV
Notes HUPBA; no menciona Approved no
Call Number (up) Admin @ si @ BEP2023 Serial 3829
Permanent link to this record