| Records |
Author  |
Oriol Pujol; Petia Radeva |
| Title |
Lumen Detection in Ivus Image Using Snakes in a Statical Framework. |
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| Year |
2002 |
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XX Congreso Anual de la Sociedad Española de Ingenieria Biomedica CASEIB 2002, 1: 129–132. |
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Saragossa, Espanya |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ PuR2002 |
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315 |
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| Author |
Oriol Pujol; Petia Radeva |
| Title |
Optimal extension of Error Correcting Output Codes |
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2006 |
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Perpignan (France) |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPR2006d |
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695 |
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Oriol Pujol; Petia Radeva; J. Mauri; E Fernandez-Nofrerias |
| Title |
Automatic segmentation of lumen in Intravascular Ultrasound Images: An evaluation of texture feature extractors. |
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2002 |
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Iberoamerican Conference on Artificial Intelligence IBERAMIA 2002, Springer Verlag |
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Sevilla, Espanya |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ PRM2002 |
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314 |
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| Author |
Oriol Pujol; Petia Radeva; Jordi Vitria |
| Title |
Traffic sign recognition using an adaptive boosting multiclass framework |
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Miscellaneous |
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2005 |
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Workshop sobre Reconocimiento de Formas y Analisis de Imagenes (AERFAI), I Congreso Español de Informatica (CEDI´2005) |
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Granada (Spain) |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ PRV2005 |
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618 |
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| Author |
Oriol Pujol; Petia Radeva; Jordi Vitria; J. Mauri |
| Title |
Adaboost to Classify Plaque Appearance in IVUS Images |
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Miscellaneous |
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2004 |
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Progress in Pattern Recognition, Image Analysis and Applications, LNCS 3287:629–636 |
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Puebla (Mexico) |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ PRV2004 |
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472 |
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| Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
| Title |
A flexible outlier detector based on a topology given by graph communities |
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2020 |
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Arxiv |
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Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings. |
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IAM; DAG; 600.139; 600.145; 600.140; 600.121 |
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Admin @ si @ RBG2020 |
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3475 |
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| Author |
Oriol Ramos Terrades; Ernest Valveny |
| Title |
Line Detection Using Ridgelets Transform for Graphic Symbol Representation |
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2003 |
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In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science 2652:829–837 |
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Springer-Verlag |
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DAG |
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DAG @ dag @ RaV2003a |
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403 |
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| Author |
Oriol Ramos Terrades; Ernest Valveny |
| Title |
Indexing Technical Symbols Using Ridgelets Transform |
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Miscellaneous |
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2003 |
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Proceedings of the Fifth International Workshop on Graphics Recognition (GREC´03), 202–211 |
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DAG |
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DAG @ dag @ RaV2003c |
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405 |
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| Author |
Oriol Ramos Terrades; Ernest Valveny |
| Title |
Radon Transform for Lineal Symbol Representation |
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2003 |
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Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR´03), 195–199 |
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DAG |
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DAG @ dag @ RaV2003d |
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406 |
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Oriol Ramos Terrades; Ernest Valveny |
| Title |
Indexing Technical Symbols Using Ridgelets Transform |
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2004 |
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Graphics Recognition: Recent Advances and Perspectives, J. Llados, Y.B. Kwon (Eds.), Lecture Notes in Computer Science, 3088:177–187, ISBN: 3–540–22478–5 |
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DAG |
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DAG @ dag @ VaD2004c |
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503 |
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Oriol Ramos Terrades; Ernest Valveny |
| Title |
Local Norm Features based on ridgelets Transform |
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Miscellaneous |
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2005 |
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8th International Conference on Document Analysis and Recognition (ICDAR´05), 700–704 |
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DAG |
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DAG @ dag @ RaV2005d |
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642 |
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Oriol Ramos Terrades; Salvatore Tabbone; Ernest Valveny |
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Combination of shape descriptors using an adaptation of boosting |
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2006 |
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18th International Conference on Pattern Recognition (ICPR´06), 2: 764–767 |
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Hong Kong |
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DAG |
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DAG @ dag @ RTV2006 |
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718 |
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Oriol Ramos Terrades; Salvatore Tabbone; L. Wendling; Ernest Valveny |
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Symbol Recognition based on a Multiresolution Analysis of the Radon Transform |
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2004 |
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The International Workshop on Multidisciplinary Image, Video, and Audio Retrieval and Mining |
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Sherbrooke (Canada) |
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DAG |
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DAG @ dag @ RTW2004 |
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500 |
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Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen |
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Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images |
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2023 |
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Arxiv |
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107241 |
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This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori. |
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IAM |
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Admin @ si @ CCG2023 |
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3855 |
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| Author |
Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes |
| Title |
Learning Graph Edit Distance by Graph NeuralNetworks |
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Miscellaneous |
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2020 |
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Arxiv |
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The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. |
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DAG; 600.121; 600.140; 601.302 |
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Admin @ si @ RFL2020 |
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3555 |
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