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Author 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 David Geronimo; Angel Sappa; Antonio Lopez; Daniel Ponsa edit   pdf
url  openurl
  Title Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection Type Conference Article
  Year 2007 Publication Proceedings of the 5th International Conference on Computer Vision Systems Abbreviated Journal ICVS  
  Volume Issue Pages  
  Keywords Pedestrian Detection  
  Abstract On–board pedestrian detection is in the frontier of the state–of–the–art since it implies processing outdoor scenarios from a mobile platform and searching for aspect–changing objects in cluttered urban environments. Most promising approaches include the development of classifiers based on feature selection and machine learning. However, they use a large number of features which compromises real–time. Thus, methods for running the classifiers in only a few image windows must be provided. In this paper we contribute in both aspects, proposing a camera
pose estimation method for adaptive sparse image sampling, as well as a classifier for pedestrian detection based on Haar wavelets and edge orientation histograms as features and AdaBoost as learning machine. Both proposals are compared with relevant approaches in the literature, showing comparable results but reducing processing time by four for the sampling tasks and by ten for the classification one.		  
  Address Bielefeld (Germany)  
  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 @ gsl2007a Serial 786  
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Author David Geronimo; Antonio Lopez; Daniel Ponsa; Angel Sappa edit   pdf
url  openurl
  Title Haar Wavelets and Edge Orientation Histograms for On-Board Pedestrian Detection Type Conference Article
  Year 2007 Publication 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 Abbreviated Journal  
  Volume 1 Issue Pages 418–425  
  Keywords Pedestrian detection  
  Abstract  
  Address Girona (Spain)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor J. Marti et al.  
  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 @ GLP2007a Serial 805  
Permanent link to this record
 
 
Author David Geronimo; Antonio Lopez; Angel Sappa edit   pdf
url  openurl
  Title Computer Vision Approaches for Pedestrian Detection: Visible Spectrum Survey Type Conference Article
  Year 2007 Publication 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 Abbreviated Journal  
  Volume 1 Issue Pages 547–554  
  Keywords Pedestrian detection  
  Abstract Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. There are many proposals in the literature but they lack a comparative viewpoint. According to this, in this paper we first propose a common framework where we fit the different approaches, and second we use this framework to provide a comparative point of view of the details of such different approaches, pointing out also the main challenges to be solved in the future. In summary, we expect
this survey to be useful for both novel and experienced researchers in the field. In the first case, as a clarifying snapshot of the state of the art; in the second, as a way to unveil trends and to take conclusions from the comparative study.		  
  Address Girona (Spain)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor J. Marti et al.  
  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 @ GLS2007 Serial 804  
Permanent link to this record
 
 
Author J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa edit   pdf
url  doi
isbn  openurl
  Title Visible-Thermal Fusion based Monocular Visual Odometry Type Conference Article
  Year 2015 Publication 2nd Iberian Robotics Conference ROBOT2015 Abbreviated Journal  
  Volume 417 Issue Pages 517-528  
  Keywords Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion.  
  Abstract The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.		  
  Address Lisboa; Portugal; November 2015  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2194-5357 ISBN 978-3-319-27145-3 Medium  
  Area Expedition Conference ROBOT  
  Notes ADAS; 600.076; 600.086 Approved no  
  Call Number Admin @ si @ PAD2015 Serial 2663  
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Author Diego Porres edit   pdf
url  openurl
  Title Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks Type Conference Article
  Year 2021 Publication Machine Learning for Creativity and Design, Neurips Workshop Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at this https URL.  
  Address Virtual; December 2021  
  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 NEURIPSW  
  Notes ADAS; 601.365 Approved no  
  Call Number Admin @ si @ Por2021 Serial 3597  
Permanent link to this record
 
 
Author Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska edit   pdf
url  openurl
  Title Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation Type Conference Article
  Year 2018 Publication International MICCAI Brainlesion Workshop Abbreviated Journal  
  Volume 11384 Issue Pages 393-405  
  Keywords Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution  
  Abstract In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge.  
  Address  
  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 MICCAIW  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ PSH2018 Serial 3251  
Permanent link to this record
 
 
Author Idoia Ruiz; Joan Serrat edit   pdf
url  doi
openurl 
  Title Rank-based ordinal classification Type Conference Article
  Year 2020 Publication 25th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 8069-8076  
  Keywords  
  Abstract Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class labels. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1. . . C, being C the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to least likely. This is thanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing
that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, i.e. most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset.		  
  Address Virtual; January 2021  
  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 ICPR  
  Notes ADAS; 600.118; 600.124 Approved no  
  Call Number Admin @ si @ RuS2020 Serial 3549  
Permanent link to this record
 
 
Author Javad Zolfaghari Bengar; Abel Gonzalez-Garcia; Gabriel Villalonga; Bogdan Raducanu; Hamed H. Aghdam; Mikhail Mozerov; Antonio Lopez; Joost Van de Weijer edit   pdf
url  doi
openurl 
  Title Temporal Coherence for Active Learning in Videos Type Conference Article
  Year 2019 Publication IEEE International Conference on Computer Vision Workshops Abbreviated Journal  
  Volume Issue Pages 914-923  
  Keywords  
  Abstract Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used to define the nodes of a graph and tracked forward and backward to temporally link the nodes. Minimizing an energy function defined on this graphical model provides estimates of both false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active learning for video object detection in road scenes. Finally, we show that our approach outperforms active learning baselines tested on two datasets.  
  Address Seul; Corea; October 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 ICCVW  
  Notes LAMP; ADAS; 600.124; 602.200; 600.118; 600.120; 600.141 Approved no  
  Call Number Admin @ si @ ZGV2019 Serial 3294  
Permanent link to this record
 
 
Author Hamed H. Aghdam; Abel Gonzalez-Garcia; Joost Van de Weijer; Antonio Lopez edit   pdf
url  doi
openurl 
  Title Active Learning for Deep Detection Neural Networks Type Conference Article
  Year 2019 Publication 18th IEEE International Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages 3672-3680  
  Keywords  
  Abstract The cost of drawing object bounding boxes (ie labeling) for millions of images is prohibitively high. For instance, labeling pedestrians in a regular urban image could take 35 seconds on average. Active learning aims to reduce the cost of labeling by selecting only those images that are informative to improve the detection network accuracy. In this paper, we propose a method to perform active learning of object detectors based on convolutional neural networks. We propose a new image-level scoring process to rank unlabeled images for their automatic selection, which clearly outperforms classical scores. The proposed method can be applied to videos and sets of still images. In the former case, temporal selection rules can complement our scoring process. As a relevant use case, we extensively study the performance of our method on the task of pedestrian detection. Overall, the experiments show that the proposed method performs better than random selection.  
  Address Seul; Korea; October 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 ICCV  
  Notes ADAS; LAMP; 600.124; 600.109; 600.141; 600.120; 600.118 Approved no  
  Call Number Admin @ si @ AGW2019 Serial 3321  
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