| Records |
Author  |
Misael Rosales; Petia Radeva; J. Mauri; Oriol Pujol |
| Title |
Simulation Model of Intravascular Ultrasound Images |
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Miscellaneous |
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2004 |
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MICCAI, 2004, Saint Melo, France |
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Springer Verlag |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ RRM2004b |
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464 |
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Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Ruben Ballester; Claudio Battiloro; Guillermo Bernardez; Tolga Birdal; Aiden Brent; Peter Chin; Sergio Escalera; Simone Fiorellino; Odin Hoff Gardaa; Gurusankar Gopalakrishnan; Devendra Govil; Josef Hoppe; Maneel Reddy Karri; Jude Khouja; Manuel Lecha; Neal Livesay; Jan Meibner; Soham Mukherjee; Alexander Nikitin; Theodore Papamarkou; Jaro Prilepok; Karthikeyan Natesan Ramamurthy; Paul Rosen; Aldo Guzman-Saenz; Alessandro Salatiello; Shreyas N. Samaga; Simone Scardapane; Michael T. Schaub; Luca Scofano; Indro Spinelli; Lev Telyatnikov; Quang Truong; Robin Walters; Maosheng Yang; Olga Zaghen; Ghada Zamzmi; Ali Zia; Nina Miolane |
| Title |
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains |
Type |
Miscellaneous |
| Year |
2024 |
Publication |
Arxiv |
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We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelx is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at this https URL. |
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HUPBA |
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Admin @ si @ HPF2024 |
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4021 |
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N. Pares; J.R. Serra |
| Title |
Tailleur: El problema del sastre. |
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1992 |
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V Simposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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Admin @ si @ PaS1992 |
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252 |
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N. Zakaria; Jean-Marc Ogier; Josep Llados |
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On-line Graphics Recognition based on Invariant Spatio-Sequential Descriptor: Fuzzy Matrix |
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Miscellaneous |
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2005 |
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Sixth IAPR International Workshop on Graphics Recognition (GREC 2005), 248–259 |
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Hong Kong (China) |
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DAG |
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DAG @ dag @ YFY2005b |
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622 |
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| Author |
O. Fors; Xavier Otazu; J. Nuñez |
| Title |
Fusion Mediante Wavelets de Imagenes Spot-pan y del Satelite Tailandes TMSAT. |
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Miscellaneous |
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2001 |
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Teledeteccion, Medio Ambiente y Cambio Global, IX Congreso Nacional de Teledeteccion, 546–550. |
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Lleida |
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CIC |
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CAT @ cat @ FON2001 |
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94 |
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| Author |
O. Rodriguez; David Rotger; J. Mauri; E. Fernandez; V. Valle; Petia Radeva |
| Title |
Active vessel workstation: three-dimensional reconstruction of coronary arteries by fusion of angiography and intravascular ultrasound |
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Miscellaneous |
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2004 |
Publication |
European Society of Cardiology Congress 2004 |
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Munich |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ RRM2004a |
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479 |
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| Author |
Oriol Pujol; David Rotger; Petia Radeva; O. Rodriguez; J. Mauri |
| Title |
Near Real Time Plaque Segmentation of IVUS |
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Miscellaneous |
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2003 |
Publication |
Proceedings of Computers in Cardiology |
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Thessaloniki |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ PRR2003b |
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401 |
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| Author |
Oriol Pujol; Misael Rosales; Petia Radeva; E Fernandez-Nofrerias |
| Title |
Intravascular Ultrasound Images Vessel Characterization using AdaBoost |
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Miscellaneous |
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2003 |
Publication |
Proceedings on FIMH |
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Lyon, France |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ PRR2003a |
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400 |
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| Author |
Oriol Pujol; Oriol Rodriguez-Leor; J. Mauri; E. Fernandez; V. Valle; Petia Radeva |
| Title |
Automatic segmentation and characterization of IVUS images by texture analysis |
Type |
Miscellaneous |
| Year |
2004 |
Publication |
European Society of Cardiology Congress 2004 |
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Munich |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ PRM2004 |
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478 |
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| Author |
Oriol Pujol; Petia Radeva |
| Title |
Optimal extension of Error Correcting Output Codes |
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Miscellaneous |
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2006 |
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Perpignan (France) |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2006d |
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695 |
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| Author |
Oriol Pujol; Petia Radeva |
| Title |
Lumen Detection in Ivus Image Using Snakes in a Statical Framework. |
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Miscellaneous |
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2002 |
Publication |
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; J. Mauri; E Fernandez-Nofrerias |
| Title |
Automatic segmentation of lumen in Intravascular Ultrasound Images: An evaluation of texture feature extractors. |
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Miscellaneous |
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2002 |
Publication |
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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no |
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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 |
Publication |
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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Miscellaneous |
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2020 |
Publication |
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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