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Author (down) Oriol Pujol; David Rotger; Petia Radeva; O. Rodriguez; J. Mauri edit  openurl
  Title Near Real Time Plaque Segmentation of IVUS Type Miscellaneous
  Year 2003 Publication Proceedings of Computers in Cardiology Abbreviated Journal  
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  Abstract  
  Address Thessaloniki  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ PRR2003b Serial 401  
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Author (down) O. Rodriguez; David Rotger; J. Mauri; E. Fernandez; V. Valle; Petia Radeva edit  openurl
  Title Active vessel workstation: three-dimensional reconstruction of coronary arteries by fusion of angiography and intravascular ultrasound Type Miscellaneous
  Year 2004 Publication European Society of Cardiology Congress 2004 Abbreviated Journal  
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  Address Munich  
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  Notes MILAB Approved no  
  Call Number BCNPCL @ bcnpcl @ RRM2004a Serial 479  
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Author (down) O. Fors; Xavier Otazu; J. Nuñez edit  openurl
  Title Fusion Mediante Wavelets de Imagenes Spot-pan y del Satelite Tailandes TMSAT. Type Miscellaneous
  Year 2001 Publication Teledeteccion, Medio Ambiente y Cambio Global, IX Congreso Nacional de Teledeteccion, 546–550. Abbreviated Journal  
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  Keywords  
  Abstract  
  Address Lleida  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ FON2001 Serial 94  
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Author (down) N. Zakaria; Jean-Marc Ogier; Josep Llados edit  openurl
  Title On-line Graphics Recognition based on Invariant Spatio-Sequential Descriptor: Fuzzy Matrix Type Miscellaneous
  Year 2005 Publication Sixth IAPR International Workshop on Graphics Recognition (GREC 2005), 248–259 Abbreviated Journal  
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  Abstract  
  Address Hong Kong (China)  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ YFY2005b Serial 622  
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Author (down) N. Pares; J.R. Serra edit  openurl
  Title Tailleur: El problema del sastre. Type Miscellaneous
  Year 1992 Publication V Simposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. Abbreviated Journal  
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  Notes Approved no  
  Call Number Admin @ si @ PaS1992 Serial 252  
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Author (down) 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 edit   pdf
url  openurl
  Title TopoX: A Suite of Python Packages for Machine Learning on Topological Domains Type Miscellaneous
  Year 2024 Publication Arxiv Abbreviated Journal  
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  Abstract 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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  Notes HUPBA Approved no  
  Call Number Admin @ si @ HPF2024 Serial 4021  
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Author (down) Misael Rosales; Petia Radeva; J. Mauri; Oriol Pujol edit  url
openurl 
  Title Simulation Model of Intravascular Ultrasound Images Type Miscellaneous
  Year 2004 Publication MICCAI, 2004, Saint Melo, France Abbreviated Journal  
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  Address Springer Verlag  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ RRM2004b Serial 464  
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Author (down) Misael Rosales edit  openurl
  Title Empirical Simulation Moldel of Intravascular Ultrasound Type Miscellaneous
  Year 2002 Publication Director: P. Radeva, Master Thesis. Abbreviated Journal  
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  Address CVC (UAB)  
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  Notes Approved no  
  Call Number Admin @ si @ Ros2002 Serial 323  
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Author (down) Miquel Ferrer; Robert Benavente; Ernest Valveny; J. Garcia; Agata Lapedriza; Gemma Sanchez edit  openurl
  Title Aprendizaje Cooperativo Aplicado a la Docencia de las Asignaturas de Programacion en Ingenieria Informatica Type Miscellaneous
  Year 2008 Publication Octava Jornada sobre Aprendizaje Cooperativo, 41–46 Abbreviated Journal  
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  Address Lleida (Spain).  
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  Notes OR;DAG;CIC;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ FBV2008 Serial 955  
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Author (down) Mikhail Mozerov; V. Kober; I.A. Ovseyevich edit  openurl
  Title A Stereo Matching Algorithm with Global Smoothness Criterion Type Miscellaneous
  Year 2006 Publication Topical Meeting on Optoinformatics / Information Photonics, 133–135 Abbreviated Journal  
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  Address Saint-Petersburg (Russia)  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ MKO2006 Serial 675  
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Author (down) Mikel Menta; Adriana Romero; Joost Van de Weijer edit   pdf
openurl 
  Title Learning to adapt class-specific features across domains for semantic segmentation Type Miscellaneous
  Year 2020 Publication Arxiv Abbreviated Journal  
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  Abstract arXiv:2001.08311
Recent advances in unsupervised domain adaptation have shown the effectiveness of adversarial training to adapt features across domains, endowing neural networks with the capability of being tested on a target domain without requiring any training annotations in this domain. The great majority of existing domain adaptation models rely on image translation networks, which often contain a huge amount of domain-specific parameters. Additionally, the feature adaptation step often happens globally, at a coarse level, hindering its applicability to tasks such as semantic segmentation, where details are of crucial importance to provide sharp results. In this thesis, we present a novel architecture, which learns to adapt features across domains by taking into account per class information. To that aim, we design a conditional pixel-wise discriminator network, whose output is conditioned on the segmentation masks. Moreover, following recent advances in image translation, we adopt the recently introduced StarGAN architecture as image translation backbone, since it is able to perform translations across multiple domains by means of a single generator network. Preliminary results on a segmentation task designed to assess the effectiveness of the proposed approach highlight the potential of the model, improving upon strong baselines and alternative designs.		  
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  Notes LAMP; 600.120 Approved no  
  Call Number Admin @ si @ MRW2020 Serial 3545  
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Author (down) Michael Villamizar; A. Sanfeliu; Juan Andrade edit  openurl
  Title Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection Type Miscellaneous
  Year 2006 Publication 18th International Conference on Pattern Recognition, 81–85 Abbreviated Journal  
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  Address Hong Kong  
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  Notes Approved no  
  Call Number Admin @ si @ VSA2006a Serial 663  
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Author (down) Michael Villamizar; A. Sanfeliu; Juan Andrade edit  openurl
  Title Orientation Invariant Features for Multiclass Object Recognition Type Miscellaneous
  Year 2006 Publication 11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 655–664 Abbreviated Journal  
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  Address Cancun (Mexico)  
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  Notes Approved no  
  Call Number Admin @ si @ VSA2006b Serial 664  
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Author (down) Mert Kilickaya; Joost van de Weijer; Yuki M. Asano edit   pdf
url  openurl
  Title Towards Label-Efficient Incremental Learning: A Survey Type Miscellaneous
  Year 2023 Publication Arxiv Abbreviated Journal  
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  Abstract The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence. Such an approach is non-incremental, as it assumes access to all images of all categories at once. However, for many applications, non-incremental learning is unrealistic. To that end, researchers study incremental learning, where a learner is required to adapt to an incoming stream of data with a varying distribution while preventing forgetting of past knowledge. Significant progress has been made, however, the vast majority of works focus on the fully supervised setting, making these algorithms label-hungry thus limiting their real-life deployment. To that end, in this paper, we make the first attempt to survey recently growing interest in label-efficient incremental learning. We identify three subdivisions, namely semi-, few-shot- and self-supervised learning to reduce labeling efforts. Finally, we identify novel directions that can further enhance label-efficiency and improve incremental learning scalability. Project website: this https URL.  
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  Notes LAMP Approved no  
  Call Number Admin @ si @ KWA2023 Serial 3994  
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Author (down) Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Antonio Moreno; Petia Radeva; Domenec Puig edit  openurl
  Title CuisineNet: Food Attributes Classification using Multi-scale Convolution Network. Type Miscellaneous
  Year 2018 Publication Arxiv Abbreviated Journal  
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  Abstract Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.  
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  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ KJR2018 Serial 3235  
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