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Author |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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Title  |
Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos |
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2015 |
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Arxiv |
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Egocentric images offer a hands-free way to record daily experiences and special events, where social interactions are of special interest. A natural question that arises is how to extract and track the appearance of multiple persons in a social event captured by a wearable camera. In this paper, we propose a novel method to find correspondences of multiple-faces in low temporal resolution egocentric sequences acquired through a wearable camera. This kind of sequences imposes additional challenges to the multitracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution (2 fpm), abrupt changes in the field of view, in illumination conditions and in the target location are very frequent. To overcome such a difficulty, we propose to generate, for each detected face, a set of correspondences along the whole sequence that we call tracklet and to take advantage of their redundancy to deal with both false positive face detections and unreliable tracklets. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which are aimed to correspond to specific persons. Finally, a prototype tracklet is extracted for each eBoT. We validated our method over a dataset of 18.000 images from 38 egocentric sequences with 52 trackable persons and compared to the state-of-the-art methods, demonstrating its effectiveness and robustness. |
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MILAB |
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Admin @ si @ ADR2015b |
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2713 |
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Author |
Antonio Lopez; David Lloret; Joan Serrat; Juan J. Villanueva |
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Title |
Multilocal Creaseness Based on the Level-Set Extrinsic Curvarture. |
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2000 |
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Computer Vision and Image Understanding, Academic Press, 77(2):111–144. |
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ADAS @ adas @ LLS 2000 b |
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172 |
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Maya Dimitrova; Ch. Roumenin; Petia Radeva; David Rotger; Juan J. Villanueva |
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Title |
Multimodal Intelligent System for Cardiovascular Diagnosis |
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2003 |
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Automation and Informatics, any XXXVII, num. 3 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ DRR2003 |
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374 |
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David Rotger |
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Title |
Multimodal Registration of Intravascular Ultrasound Images and Angiography |
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2002 |
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Director: P. Radeva, Master Thesis. |
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Admin @ si @ Rot2002 |
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324 |
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Author |
David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri |
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Title |
Multimodal Registration of Intravascular Ultrasound Images and Angiography. |
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Miscellaneous |
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2002 |
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XX Congreso Anual de la Sociedad Española de Ingenieria Biomedica CASEIB 2002, 1: 137–140. |
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Zaragoza, Espanya |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ RRF2002b |
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317 |
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Author |
J.M. Sanchez; X. Binefa; J.R. Kender |
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Title |
Multiple Feature Temporal Models for Object Detection in Video. |
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2002 |
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Proceeding of the International Conference on Multimedia and Expo ICME 2002 |
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Lausanne |
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Admin @ si @ SBK2002b |
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299 |
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Author |
Daniel Ponsa; Antonio Lopez; Joan Serrat; Felipe Lumbreras; T. Graf |
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Title |
Multiple Vehicle 3D Tracking Using an Unscented Kalman Filter |
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2005 |
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Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 1108–1113, ISBN:0–7803–9216–7 |
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vehicle detection |
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Vienna (Austria) |
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ADAS @ adas @ PLS2005 |
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615 |
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Author |
Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
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Title |
Multiresolution colour texture representations for tile classification |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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ADAS;CIC |
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ADAS @ adas @ LBV1999a |
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3 |
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Author |
Juan Andrade; T. Alejandra Vidal; A. Sanfeliu |
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Title |
Multirobot C-SLAM: Simultaneous localization, control, and mapping |
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2005 |
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in Proc. IEEE ICRA05 Workshop on Network Robot Systems |
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Barcelona (Spain) |
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Admin @ si @ AVS2005b |
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549 |
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Author |
Oriol Pujol; David Rotger; Petia Radeva; O. Rodriguez; J. Mauri |
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Title |
Near Real Time Plaque Segmentation of IVUS |
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2003 |
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Proceedings of Computers in Cardiology |
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Thessaloniki |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ PRR2003b |
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401 |
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Fernando Vilariño; Enric Marti |
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New didactic techniques in the EHES applying mobile technologies |
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2008 |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestió d’Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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MILAB;IAM;MV;SIAI |
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IAM @ iam @ VIM2008 |
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1664 |
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Antonio Lopez; W. Niessen; Joan Serrat; K. Nicolay; Bart M. Ter Haar Romeny; Juan J. Villanueva; M. Viergever |
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New improvements in the multiscale analysis of trabecular bone patterns. |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes (SNRFAI’99), pags. 497–504 |
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ADAS @ adas @ LNS1999 |
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17 |
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Antonio Lopez; W. Niessen; Joan Serrat; K. Nicolay; Bart M. Ter Haar Romeny; Juan J. Villanueva; M. Viergever |
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New improvements in the multiscale analysis of trabecular bone patterns. |
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2000 |
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Pattern Recognition and Applications, IOS Press, 251–260. |
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ADAS @ adas @ LNS2000 |
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332 |
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Adriana Romero; Petia Radeva; Carlo Gatta |
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No more meta-parameter tuning in unsupervised sparse feature learning |
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2014 |
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Arxiv |
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CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well. |
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MILAB; LAMP; 600.079 |
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Admin @ si @ RRG2014 |
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2471 |
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Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez |
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Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest |
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2016 |
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Arxiv |
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Domain Adaptation; Pedestrian detection; Random Forest |
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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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ADAS @ adas @ MVJ2016 |
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2868 |
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