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Author (up) Antonio Lopez; Joan Serrat; J. Saludes; Cristina Cañero; Felipe Lumbreras; T. Graf edit   pdf
openurl 
  Title Ridgeness for Detecting Lane Markings Type Miscellaneous
  Year 2005 Publication 2nd International Workshop on Intelligent Transportation Systems (WIT2005), Conference Proceedings (Sponsored by the IEEE Communication Society, Germany Chapter) Abbreviated Journal  
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  Keywords lane markings  
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  Address Hamburg (Germany)  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ LSS2005 Serial 548  
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Author (up) Antonio Lopez; Ricardo Toledo; Joan Serrat; Juan J. Villanueva edit  openurl
  Title Extraction of vessel centerlines from 2D coronary angiographies Type Miscellaneous
  Year 1999 Publication Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. pgs. 489–496, volume I Abbreviated Journal  
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  Address Bilbao  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ LTS1999 Serial 14  
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Author (up) Antonio Lopez; W. Niessen; Joan Serrat; K. Nicolay; Bart M. Ter Haar Romeny; Juan J. Villanueva; M. Viergever edit  openurl
  Title New improvements in the multiscale analysis of trabecular bone patterns. Type Miscellaneous
  Year 2000 Publication Pattern Recognition and Applications, IOS Press, 251–260. Abbreviated Journal  
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  Call Number ADAS @ adas @ LNS2000 Serial 332  
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Author (up) Antonio Lopez; W. Niessen; Joan Serrat; K. Nicolay; Bart M. Ter Haar Romeny; Juan J. Villanueva; M. Viergever edit  openurl
  Title New improvements in the multiscale analysis of trabecular bone patterns. Type Miscellaneous
  Year 1999 Publication Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes (SNRFAI’99), pags. 497–504 Abbreviated Journal  
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  Address Bilbao  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ LNS1999 Serial 17  
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Author (up) Arya Farkhondeh; Cristina Palmero; Simone Scardapane; Sergio Escalera edit   pdf
openurl 
  Title Towards Self-Supervised Gaze Estimation Type Miscellaneous
  Year 2022 Publication Arxiv Abbreviated Journal  
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  Abstract Recent joint embedding-based self-supervised methods have surpassed standard supervised approaches on various image recognition tasks such as image classification. These self-supervised methods aim at maximizing agreement between features extracted from two differently transformed views of the same image, which results in learning an invariant representation with respect to appearance and geometric image transformations. However, the effectiveness of these approaches remains unclear in the context of gaze estimation, a structured regression task that requires equivariance under geometric transformations (e.g., rotations, horizontal flip). In this work, we propose SwAT, an equivariant version of the online clustering-based self-supervised approach SwAV, to learn more informative representations for gaze estimation. We demonstrate that SwAT, with ResNet-50 and supported with uncurated unlabeled face images, outperforms state-of-the-art gaze estimation methods and supervised baselines in various experiments. In particular, we achieve up to 57% and 25% improvements in cross-dataset and within-dataset evaluation tasks on existing benchmarks (ETH-XGaze, Gaze360, and MPIIFaceGaze).  
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  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ FPS2022 Serial 3822  
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Author (up) Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal edit   pdf
url  openurl
  Title GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation Type Miscellaneous
  Year 2024 Publication Arxiv Abbreviated Journal  
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  Abstract Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and complex models, while achieving high accuracy, can be computationally expensive and memory-intensive, making them impractical for deployment on resource constrained devices. Knowledge distillation allows us to create small and more efficient models that retain much of the performance of their larger counterparts. Here we present a graph-based knowledge distillation framework to correctly identify and localize the document objects in a document image. Here, we design a structured graph with nodes containing proposal-level features and edges representing the relationship between the different proposal regions. Also, to reduce text bias an adaptive node sampling strategy is designed to prune the weight distribution and put more weightage on non-text nodes. We encode the complete graph as a knowledge representation and transfer it from the teacher to the student through the proposed distillation loss by effectively capturing both local and global information concurrently. Extensive experimentation on competitive benchmarks demonstrates that the proposed framework outperforms the current state-of-the-art approaches. The code will be available at: this https URL.  
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  Notes DAG Approved no  
  Call Number Admin @ si @ BBL2024b Serial 4023  
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Author (up) Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez edit   pdf
openurl 
  Title Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest Type Miscellaneous
  Year 2016 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords Domain Adaptation; Pedestrian detection; Random Forest  
  Abstract Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved.  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ MVJ2016 Serial 2868  
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Author (up) B. Moghaddam; David Guillamet; Jordi Vitria edit  openurl
  Title , Local Appearance-Based Models using High-Order Statistics of Image Features Type Miscellaneous
  Year 2003 Publication IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ MGV2003 Serial 395  
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Author (up) B. Moghaddam; David Guillamet; Jordi Vitria edit  openurl
  Title Local Appearance-Based Models using High-Order Statistics of Image Features Type Miscellaneous
  Year 2003 Publication Mitsubishi Electrical Reasearch Lab Technical Report Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ TR2003-85 Serial 396  
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Author (up) Bart M. Ter Haar Romeny; W. Niessen; J. Weickert; P. Van Roermund; W. Van Enk; Antonio Lopez; R. Maas edit  openurl
  Title Orientation detection of trabecular bone Type Miscellaneous
  Year 1996 Publication Biophysics and Molecular Biology, International Biophysics Congress. Volume 65, pgs. P–H5–43 Abbreviated Journal  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ HNW1996 Serial 489  
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Author (up) Bhaskar Chakraborty edit  openurl
  Title View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component Wise HMM of Body Parts Type Miscellaneous
  Year 2008 Publication CVC Technical Report #123 Abbreviated Journal  
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  Address Barcelona, Spain  
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  Notes ISE Approved no  
  Call Number Admin @ si @ Cha2008 Serial 1149  
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Author (up) Bogdan Raducanu; Jordi Vitria edit  openurl
  Title Aprendiendo a Aprender: de Maquinas Listas a Maquinas Inteligentes Type Miscellaneous
  Year 2006 Publication Campus Multidisciplinario en Percepcion e Inteligencia (Antionio Fernandez–Caballero et al., eds.), 1: 34–45 Abbreviated Journal  
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  Address Albacete (Spain)  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RaV2006b Serial 714  
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Author (up) Bogdan Raducanu; Jordi Vitria edit  openurl
  Title Real-Time Face Tracking for Context-Aware Computing Type Miscellaneous
  Year 2005 Publication 8th Catalan Conference on Artificial Intelligence (CCIA 2005) (published in Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RaV2005a Serial 560  
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Author (up) Bonifaz Stuhr; Jurgen Brauer; Bernhard Schick; Jordi Gonzalez edit   pdf
url  openurl
  Title Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation Type Miscellaneous
  Year 2023 Publication Arxiv Abbreviated Journal  
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  Abstract A common goal of unpaired image-to-image translation is to preserve content consistency between source images and translated images while mimicking the style of the target domain. Due to biases between the datasets of both domains, many methods suffer from inconsistencies caused by the translation process. Most approaches introduced to mitigate these inconsistencies do not constrain the discriminator, leading to an even more ill-posed training setup. Moreover, none of these approaches is designed for larger crop sizes. In this work, we show that masking the inputs of a global discriminator for both domains with a content-based mask is sufficient to reduce content inconsistencies significantly. However, this strategy leads to artifacts that can be traced back to the masking process. To reduce these artifacts, we introduce a local discriminator that operates on pairs of small crops selected with a similarity sampling strategy. Furthermore, we apply this sampling strategy to sample global input crops from the source and target dataset. In addition, we propose feature-attentive denormalization to selectively incorporate content-based statistics into the generator stream. In our experiments, we show that our method achieves state-of-the-art performance in photorealistic sim-to-real translation and weather translation and also performs well in day-to-night translation. Additionally, we propose the cKVD metric, which builds on the sKVD metric and enables the examination of translation quality at the class or category level.  
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  Notes ISE Approved no  
  Call Number Admin @ si @ SBS2023 Serial 3863  
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Author (up) C. Mariño; M.G. Penas; M. Penedo; David Lloret; M.J. Carreira edit  openurl
  Title Integration of Mutual Information and Creaseness Based Methods for the Automatic Registration of SLO Sequences. Type Miscellaneous
  Year 2001 Publication Proceedings of the SIARP´2001. Abbreviated Journal  
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  Address Brasil.  
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  Notes Approved no  
  Call Number Admin @ si @ MPP2001 Serial 197  
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