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Author Guillermo Torres; Jan Rodríguez Dueñas; Sonia Baeza; Antoni Rosell; Carles Sanchez; Debora Gil
Title Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images Type Conference Article
Year 2023 Publication (up) 7th Workshop on Digital Image Processing for Medical and Automotive Industry in the framework of SYNASC 2023 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This study shows the generation process and the subsequent study of the representation space obtained by extracting GLCM texture features from computer-aided tomography (CT) scans of pulmonary nodules (PN). For this, data from 92 patients from the Germans Trias i Pujol University Hospital were used. The workflow focuses on feature extraction using Pyradiomics and the VGG16 Convolutional Neural Network (CNN). The aim of the study is to assess whether the data obtained have a positive impact on the diagnosis of lung cancer (LC). To design a machine learning (ML) model training method that allows generalization, we train SVM and neural network (NN) models, evaluating diagnosis performance using metrics defined at slice and nodule level.
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 DIPMAI
Notes IAM Approved no
Call Number Admin @ si @ TRB2023 Serial 3926
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Author Bogdan Raducanu; Jordi Vitria
Title Real-Time Face Tracking for Context-Aware Computing Type Miscellaneous
Year 2005 Publication (up) 8th Catalan Conference on Artificial Intelligence (CCIA 2005) (published in 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 OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ RaV2005a Serial 560
Permanent link to this record
 
 
Author Oualid M. Benkarim; Petia Radeva; Laura Igual
Title Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation Type Conference Article
Year 2014 Publication (up) 8th Conference on Articulated Motion and Deformable Objects Abbreviated Journal
Volume 8563 Issue Pages 138-147
Keywords MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classi cation
Abstract The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems.
Address Palma de Mallorca; July 2014
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-319-08848-8 Medium
Area Expedition Conference AMDO
Notes MILAB; OR Approved no
Call Number Admin @ si @ BRI2014 Serial 2494
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Author Marc Bolaños; Maite Garolera; Petia Radeva
Title Video Segmentation of Life-Logging Videos Type Conference Article
Year 2014 Publication (up) 8th Conference on Articulated Motion and Deformable Objects Abbreviated Journal
Volume 8563 Issue Pages 1-9
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 AMDO
Notes MILAB Approved no
Call Number Admin @ si @ BGR2014 Serial 2558
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Author Olivier Penacchio; Laura Dempere-Marco; Xavier Otazu
Title A Neurodynamical Model Of Brightness Induction In V1 Following Static And Dynamic Contextual Influences Type Abstract
Year 2012 Publication (up) 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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ISSN ISBN Medium
Area Expedition Conference FENS
Notes CIC Approved no
Call Number Admin @ si @ PDO2012b Serial 2181
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Author Joan Mas; Gemma Sanchez; Josep Llados
Title SSP: Sketching slide Presentations, a Syntactic Approach Type Conference Article
Year 2009 Publication (up) 8th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The design of a slide presentation is a creative process. In this process first, humans visualize in their minds what they want to explain. Then, they have to be able to represent this knowledge in an understandable way. There exists a lot of commercial software that allows to create our own slide presentations but the creativity of the user is rather limited. In this article we present an application that allows the user to create and visualize a slide presentation from a sketch. A slide may be seen as a graphical document or a diagram where its elements are placed in a particular spatial arrangement. To describe and recognize slides a syntactic approach is proposed. This approach is based on an Adjacency Grammar and a parsing methodology to cope with this kind of grammars. The experimental evaluation shows the performance of our methodology from a qualitative and a quantitative point of view. Six different slides containing different number of symbols, from 4 to 7, have been given to the users and they have drawn them without restrictions in the order of the elements. The quantitative results give an idea on how suitable is our methodology to describe and recognize the different elements in a slide.
Address La Rochelle; France; July 2009
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 GREC
Notes DAG Approved no
Call Number DAG @ dag @ MSL2009a Serial 1441
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Author Salim Jouili; Salvatore Tabbone; Ernest Valveny
Title Comparing Graph Similarity Measures for Graphical Recognition. Type Conference Article
Year 2009 Publication (up) 8th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique.
Address La Rochelle; France; July 2009
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference GREC
Notes DAG Approved no
Call Number DAG @ dag @ JTV2009 Serial 1442
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Author Mathieu Nicolas Delalandre; Jean-Yves Ramel; Ernest Valveny; Muhammad Muzzamil Luqman
Title A Performance Characterization Algorithm for Symbol Localization Type Conference Article
Year 2009 Publication (up) 8th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages 3-11
Keywords
Abstract In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols).
Address La Rochelle; July 2009
Corporate Author Thesis
Publisher Springer 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 GREC
Notes DAG Approved no
Call Number DAG @ dag @ DRV2009 Serial 1443
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Author Marçal Rusiñol; K. Bertet; Jean-Marc Ogier; Josep Llados
Title Symbol Recognition Using a Concept Lattice of Graphical Patterns Type Conference Article
Year 2009 Publication (up) 8th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this paper we propose a new approach to recognize symbols by the use of a concept lattice. We propose to build a concept lattice in terms of graphical patterns. Each model symbol is decomposed in a set of composing graphical patterns taken as primitives. Each one of these primitives is described by boundary moment invariants. The obtained concept lattice relates which symbolic patterns compose a given graphical symbol. A Hasse diagram is derived from the context and is used to recognize symbols affected by noise. We present some preliminary results over a variation of the dataset of symbols from the GREC 2005 symbol recognition contest.
Address La Rochelle; July 2009
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 GREC
Notes DAG Approved no
Call Number DAG @ dag @ RBO2009 Serial 1444
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Author Jaume Gibert; Ernest Valveny; Horst Bunke
Title Dimensionality Reduction for Graph of Words Embedding Type Conference Article
Year 2011 Publication (up) 8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition Abbreviated Journal
Volume 6658 Issue Pages 22-31
Keywords
Abstract The Graph of Words Embedding consists in mapping every graph of a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. While it shows good properties in classification problems, it suffers from high dimensionality and sparsity. These two issues are addressed in this article. Two well-known techniques for dimensionality reduction, kernel principal component analysis (kPCA) and independent component analysis (ICA), are applied to the embedded graphs. We discuss their performance compared to the classification of the original vectors on three different public databases of graphs.
Address Münster, Germany
Corporate Author Thesis
Publisher Place of Publication Editor Xiaoyi Jiang; Miquel Ferrer; Andrea Torsello
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-20843-0 Medium
Area Expedition Conference GbRPR
Notes DAG Approved no
Call Number Admin @ si @ GVB2011a Serial 1743
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Author Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez
Title Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology Type Conference Article
Year 2017 Publication (up) 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 10255 Issue Pages 287-294
Keywords Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model
Abstract Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach.
Address Faro; Portugal; June 2017
Corporate Author Thesis
Publisher Place of Publication Editor L.A. Alexandre; J.Salvador Sanchez; Joao M. F. Rodriguez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-319-58837-7 Medium
Area Expedition Conference IbPRIA
Notes DAG; 602.006; 600.097; 600.121 Approved no
Call Number Admin @ si @ RFV2017 Serial 2952
Permanent link to this record
 
 
Author Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva
Title VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering Type Conference Article
Year 2017 Publication (up) 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume Issue Pages
Keywords Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks
Abstract In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed.
Address Faro; Portugal; June 2017
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 IbPRIA
Notes MILAB; no proj Approved no
Call Number Admin @ si @ BPC2017 Serial 2939
Permanent link to this record
 
 
Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados
Title Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs Type Conference Article
Year 2017 Publication (up) 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume Issue Pages
Keywords Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines
Abstract We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction.
Address Faro; Portugal; June 2017
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 IbPRIA
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ JRL2017a Serial 2953
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 (up) 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 Admin @ si @ BeO2019b Serial 3374
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Author David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; Victor Leboran; Xose M. Pardo
Title Measuring bottom-up visual attention in eye tracking experimentation with synthetic images Type Conference Article
Year 2019 Publication (up) 8th Iberian Conference on Perception Abbreviated Journal
Volume Issue Pages
Keywords
Abstract A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor pop-out type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-of-the-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets.
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; 600.128 Approved no
Call Number Admin @ si @ BFO2019c Serial 3375
Permanent link to this record