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Author (down) Javier Varona; Juan J. Villanueva edit  openurl
  Title Neural networks as spatial filters for image processing: Neurofilters Type Report
  Year 1996 Publication Technical Report #07 Abbreviated Journal  
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  Address CVC (UAB)  
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  Notes Approved no  
  Call Number ISE @ ise @ VaV1996 Serial 95  
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Author (down) Javier Varona; Juan J. Villanueva edit  openurl
  Title NeuroFilters: Neural Networks for image Processing. Type Miscellaneous
  Year 1997 Publication Vision Systems: New image Processing Techniques and Applications Algorithms, Methods, and Components. Proceedings of the SPIE. Abbreviated Journal  
  Volume Issue Pages  
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  Address Munich  
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  Notes Approved no  
  Call Number ISE @ ise @ VaV1997a Serial 207  
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Author (down) Javier Varona; Jordi Gonzalez; Xavier Roca; Juan J. Villanueva edit  openurl
  Title iTrack: Image-based Probabilistic Tracking of People. Type Conference Article
  Year 2000 Publication 15 th International Conference on Pattern Recognition Abbreviated Journal  
  Volume 3 Issue Pages 1122-1125  
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  Address Barcelona.  
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  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes ISE Approved no  
  Call Number ISE @ ise @ VGR2000a Serial 228  
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Author (down) Javier Varona; Jordi Gonzalez; Xavier Roca; Juan J. Villanueva edit  openurl
  Title Automatic Selection of Keyframes for Activity Recognition. Type Miscellaneous
  Year 2000 Publication International Workshop on Articulated Motion and Deformable Objects ( AMDO&rsquo), 173–181. Abbreviated Journal  
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  Address Palma de Mallorca.  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ VGR2000b Serial 243  
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Author (down) Javier Varona; Jordi Gonzalez; Xavier Roca; Juan J. Villanueva edit  openurl
  Title Appearance Tracking for Video Surveillance Type Miscellaneous
  Year 2003 Publication Abbreviated Journal  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ VGR2003a Serial 358  
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Author (down) Javier Varona; Jordi Gonzalez; Xavier Roca; Juan J. Villanueva edit  openurl
  Title Appearance Tracking for Video Surveillance Type Miscellaneous
  Year 2003 Publication In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science 2652:1041–1048 Abbreviated Journal  
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  Address Springer-Verlag  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ VGR2003b Serial 425  
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Author (down) Javier Varona; Jordi Gonzalez; Ignasi Rius; Juan J. Villanueva edit  openurl
  Title Importance of Detection for Video Surveillance Applications Type Journal
  Year 2008 Publication Optical Engineering, vol. 47(8), 087201/1–9 Abbreviated Journal  
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  Notes Approved no  
  Call Number ISE @ ise @ VGR2008 Serial 998  
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Author (down) Javier Varona; Antoni Jaume-i-Capo; Jordi Gonzalez; Francisco Jose Perales edit  openurl
  Title Toward Natural Interaction through Visual Recognition of Body Gestures in Real-Time Type Journal
  Year 2008 Publication Interacting with Computers, diu 10,1016/j.intcom.2008.10.001, available on line Abbreviated Journal  
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  Notes Approved no  
  Call Number ISE @ ise @ VJG2008 Serial 1022  
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Author (down) Javier Varona; A. Pujol; Juan J. Villanueva edit  openurl
  Title Visual tracking in application domains. Type Miscellaneous
  Year 1999 Publication Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. Abbreviated Journal  
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  Address Bilbao  
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  Notes Approved no  
  Call Number ISE @ ise @ VPV1999 Serial 10  
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Author (down) Javier Varona; A. Pujol; Juan J. Villanueva edit  openurl
  Title Visual Tracking in Application Domains. Type Miscellaneous
  Year 2000 Publication Pattern Recognition and Applications, IOS Press, 99–106. Abbreviated Journal  
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  Notes Approved no  
  Call Number ISE @ ise @ VPV2000 Serial 333  
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Author (down) Javier Varona edit  openurl
  Title Seguimiento visual robusto en entornos complejos, Tesis. Type Miscellaneous
  Year 2001 Publication Abbreviated Journal  
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  Notes Approved no  
  Call Number Admin @ si @ Var2001 Serial 214  
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Author (down) Javier Selva; Anders S. Johansen; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Albert Clapes edit  doi
openurl 
  Title Video transformers: A survey Type Journal Article
  Year 2023 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 45 Issue 11 Pages 12922-12943  
  Keywords Artificial Intelligence; Computer Vision; Self-Attention; Transformers; Video Representations  
  Abstract Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However, they lack inductive biases and scale quadratically with input length. These limitations are further exacerbated when dealing with the high dimensionality introduced by the temporal dimension. While there are surveys analyzing the advances of Transformers for vision, none focus on an in-depth analysis of video-specific designs. In this survey, we analyze the main contributions and trends of works leveraging Transformers to model video. Specifically, we delve into how videos are handled at the input level first. Then, we study the architectural changes made to deal with video more efficiently, reduce redundancy, re-introduce useful inductive biases, and capture long-term temporal dynamics. In addition, we provide an overview of different training regimes and explore effective self-supervised learning strategies for video. Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity.  
  Address 1 Nov. 2023  
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  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ SJE2023 Serial 3823  
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Author (down) Javier Rodenas; Bhalaji Nagarajan; Marc Bolaños; Petia Radeva edit  url
openurl 
  Title Learning Multi-Subset of Classes for Fine-Grained Food Recognition Type Conference Article
  Year 2022 Publication 7th International Workshop on Multimedia Assisted Dietary Management Abbreviated Journal  
  Volume Issue Pages 17–26  
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  Abstract Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we introduce a new method to improve the classification of classes that are more difficult to discriminate based on Multi-Subsets learning. Using a pre-trained network, we organize classes in multiple subsets using a clustering technique. Later, we embed these subsets in a multi-head model structure. This structure has three distinguishable parts. First, we use several shared blocks to learn the generalized representation of the data. Second, we use multiple specialized blocks focusing on specific subsets that are difficult to distinguish. Lastly, we use a fully connected layer to weight the different subsets in an end-to-end manner by combining the neuron outputs. We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets. Our method was successful in learning the confused classes better and we outperformed the state-of-the-art on the three datasets.  
  Address Lisboa; Portugal; October 2022  
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  Area Expedition Conference MADiMa  
  Notes MILAB Approved no  
  Call Number Admin @ si @ RNB2022 Serial 3797  
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Author (down) Javier Marin; Sergio Escalera edit   pdf
url  openurl
  Title SSSGAN: Satellite Style and Structure Generative Adversarial Networks Type Journal Article
  Year 2021 Publication Remote Sensing Abbreviated Journal  
  Volume 13 Issue 19 Pages 3984  
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  Abstract This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce
consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area.		  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ MaE2021 Serial 3651  
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Author (down) Javier Marin; David Vazquez; David Geronimo; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Learning Appearance in Virtual Scenarios for Pedestrian Detection Type Conference Article
  Year 2010 Publication 23rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 137–144  
  Keywords Pedestrian Detection; Domain Adaptation  
  Abstract Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance.  
  Address San Francisco; CA; USA; June 2010  
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  Language English Summary Language English Original Title Learning Appearance in Virtual Scenarios for Pedestrian Detection  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN 1063-6919 ISBN 978-1-4244-6984-0 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ MVG2010 Serial 1304  
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Select All    Deselect All [101–110] << 111 112 113 114 115 116 117 118 119 120 >> [121–130]
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