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Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
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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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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 |
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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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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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 |
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 |
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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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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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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no |
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Admin @ si @ ADR2015b |
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2713 |
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Author |
David Lloret; Joan Serrat; Antonio Lopez; Juan J. Villanueva |
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Title |
Motion-induced error correction in ultrasound imaging. |
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2002 |
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1st. International Symposium on 3D Data Processing Visualization and Transmission 3DPTV. |
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ADAS |
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ADAS @ adas @ LSL2002 |
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295 |
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Carme Julia; Joan Serrat; Antonio Lopez; Felipe Lumbreras; Daniel Ponsa |
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Title |
Motion segmentation through factorization. Application to night driving assistance |
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2006 |
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International Conference on Computer Vision Theory and Applications, (2) |
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ADAS |
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ADAS @ adas @ JSL2006a |
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638 |
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Hannes Mueller; Andre Groger; Jonathan Hersh; Andrea Matranga; Joan Serrat |
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Monitoring War Destruction from Space: A Machine Learning Approach |
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2020 |
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Arxiv |
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Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques combined with data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency – only limited by the available satellite imagery – which can alleviate data limitations decisively. |
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ADAS; 600.118 |
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Admin @ si @ MGH2020 |
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3489 |
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Cristina Cañero; E Fernandez-Nofrerias; J. Mauri; Petia Radeva |
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Modelling the Acquisition Geometry of a C-arm Angiography System for 3D Reconstruction. |
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2002 |
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MILAB |
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BCNPCL @ bcnpcl @ CFM2002b |
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306 |
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Jose Manuel Alvarez; Antonio Lopez |
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Model-based road detection using shadowless features and on-line learning |
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2009 |
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BMVA one–day technical meeting on vision for automotive applications |
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road detection |
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London, UK |
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ADAS @ adas @ AlA2009 |
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1272 |
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Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Syeda Furruka Banu; Farhan Akram; Forhad U. H. Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig |
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MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network |
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2019 |
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Arxiv |
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CoRR abs/1907.00856
Skin lesion segmentation in dermoscopic images is a challenge due to their blurry and irregular boundaries. Most of the segmentation approaches based on deep learning are time and memory consuming due to the hundreds of millions of parameters. Consequently, it is difficult to apply them to real dermatoscope devices with limited GPU and memory resources. In this paper, we propose a lightweight and efficient Generative Adversarial Networks (GAN) model, called MobileGAN for skin lesion segmentation. More precisely, the MobileGAN combines 1D non-bottleneck factorization networks with position and channel attention modules in a GAN model. The proposed model is evaluated on the test dataset of the ISBI 2017 challenges and the validation dataset of ISIC 2018 challenges. Although the proposed network has only 2.35 millions of parameters, it is still comparable with the state-of-the-art. The experimental results show that our MobileGAN obtains comparable performance with an accuracy of 97.61%. |
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MILAB; no menciona |
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Admin @ si @ MRA2019 |
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3384 |
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Daniel Ponsa; Jordi Vitria |
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Mobile monitoring system using an agent-oriented approach |
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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;OR;MV |
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ADAS @ adas @ DaV1999 |
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21 |
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