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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
url  openurl
  Title Factorization with Missing and Noisy Data Type Conference Article
  Year 2006 Publication 6th International Conference on Computational Science Abbreviated Journal ICCS´06  
  Volume LNCS 3991 Issue Pages 555–562  
  Keywords  
  Abstract  
  Address Reading (United Kingdom)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2006b Serial 653  
Permanent link to this record
 
 
Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
openurl 
  Title An Iterative Multiresolution Scheme for SFM Type Conference Article
  Year 2006 Publication International Conference on Image Analysis and Recognition Abbreviated Journal ICIAR 2006  
  Volume LNCS 4141 Issue 1 Pages 804–815  
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  Abstract  
  Address  
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  Language Summary Language Original Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2006c Serial 704  
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
openurl 
  Title Motion Segmentation from Feature Trajectories with Missing Data Type Conference Article
  Year 2007 Publication 3rd. Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal IbPRIA 2007  
  Volume LNCS 4477 Issue Pages 483–490  
  Keywords  
  Abstract  
  Address Girona (Spain)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor J. Marti et al. (Eds.)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2007a Serial 814  
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Author (up) Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
openurl 
  Title An Adapted Alternation Approach for Recommender Systems Type Conference Article
  Year 2008 Publication IEEE International Conference on e–Business Engineering, Abbreviated Journal  
  Volume Issue Pages 128–135  
  Keywords  
  Abstract This paper presents an adaptation of the alternation technique to tackle the prediction task in recommender systems. These systems are widely considered in electronic commerce to help customers to find products they will probably like or dislike. As the SVD-based approaches, the proposed adapted alternation technique uses all the information stored in the system to find the predictions. The main advantage of this technique with respect to the SVD-based ones is that it can deal with missing data. Furthermore, it has a smaller computational cost. Experimental results with public data sets are provided in order to show the viability of the proposed adapted alternation approach.  
  Address Xi’an (Xina)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2008e Serial 1044  
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Author (up) Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez edit   pdf
doi  openurl
  Title Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition Type Conference Article
  Year 2016 Publication 14th European Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages 697-716  
  Keywords  
  Abstract Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including recent deep models trained on millions of manually labelled images and videos.  
  Address Amsterdam; The Netherlands; October 2016  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECCV  
  Notes ADAS; 600.076; 600.085 Approved no  
  Call Number Admin @ si @ SGV2016 Serial 2824  
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Author (up) Cesar de Souza; Adrien Gaidon; Yohann Cabon; Antonio Lopez edit   pdf
doi  openurl
  Title Procedural Generation of Videos to Train Deep Action Recognition Networks Type Conference Article
  Year 2017 Publication 30th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 2594-2604  
  Keywords  
  Abstract Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for ”Procedural Human Action Videos”. It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly
outperforming fine-tuning state-of-the-art unsupervised generative models of videos.		  
  Address Honolulu; Hawaii; July 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CVPR  
  Notes ADAS; 600.076; 600.085; 600.118 Approved no  
  Call Number Admin @ si @ SGC2017 Serial 3051  
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Author (up) Chris Bahnsen; David Vazquez; Antonio Lopez; Thomas B. Moeslund edit  url
doi  openurl
  Title Learning to Remove Rain in Traffic Surveillance by Using Synthetic Data Type Conference Article
  Year 2019 Publication 14th International Conference on Computer Vision Theory and Applications Abbreviated Journal  
  Volume Issue Pages 123-130  
  Keywords Rain Removal; Traffic Surveillance; Image Denoising  
  Abstract Rainfall is a problem in automated traffic surveillance. Rain streaks occlude the road users and degrade the overall visibility which in turn decrease object detection performance. One way of alleviating this is by artificially removing the rain from the images. This requires knowledge of corresponding rainy and rain-free images. Such images are often produced by overlaying synthetic rain on top of rain-free images. However, this method fails to incorporate the fact that rain fall in the entire three-dimensional volume of the scene. To overcome this, we introduce training data from the SYNTHIA virtual world that models rain streaks in the entirety of a scene. We train a conditional Generative Adversarial Network for rain removal and apply it on traffic surveillance images from SYNTHIA and the AAU RainSnow datasets. To measure the applicability of the rain-removed images in a traffic surveillance context, we run the YOLOv2 object detection algorithm on the original and rain-removed frames. The results on SYNTHIA show an 8% increase in detection accuracy compared to the original rain image. Interestingly, we find that high PSNR or SSIM scores do not imply good object detection performance.  
  Address Praga; Czech Republic; February 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference VISIGRAPP  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ BVL2019 Serial 3256  
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Author (up) Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo edit  url
doi  openurl
  Title LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations Type Conference Article
  Year 2015 Publication 22th IEEE International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages 178 - 181  
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  Address Quebec; Canada; September 2015  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICIP  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ AST2015 Serial 2630  
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Author (up) Cristhian A. Aguilera-Carrasco; F. Aguilera; Angel Sappa; C. Aguilera; Ricardo Toledo edit   pdf
doi  openurl
  Title Learning cross-spectral similarity measures with deep convolutional neural networks Type Conference Article
  Year 2016 Publication 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The simultaneous use of images from different spectracan be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains.  
  Address Las vegas; USA; June 2016  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CVPRW  
  Notes ADAS; 600.086; 600.076 Approved no  
  Call Number Admin @ si @AAS2016 Serial 2809  
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Author (up) Cristhian Aguilera; Fernando Barrera; Angel Sappa; Ricardo Toledo edit   pdf
openurl 
  Title A Novel SIFT-Like-Based Approach for FIR-VS Images Registration Type Conference Article
  Year 2012 Publication 11th Quantitative InfraRed Thermography Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Naples, Italy  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference QIRT  
  Notes ADAS; TV Approved no  
  Call Number Admin @ si @ ABS2012 Serial 2017  
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