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| Author | Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers | ||||
| Title | Adapting Pedestrian Detection from Synthetic to Far Infrared Images | Type | Conference Article | ||
| Year | 2013 | Publication | ICCV Workshop on Visual Domain Adaptation and Dataset Bias | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Domain Adaptation; Far Infrared; Pedestrian Detection | ||||
| Abstract | We present different techniques to adapt a pedestrian classifier trained with synthetic images and the corresponding automatically generated annotations to operate with far infrared (FIR) images. The information contained in this kind of images allow us to develop a robust pedestrian detector invariant to extreme illumination changes. | ||||
| Address | Sydney; Australia; December 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Sydney, Australy | Editor | ||
| Language | English | Summary Language | Original Title | ||
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| Area | Expedition | Conference | ICCVW-VisDA | ||
| Notes | ADAS; 600.054; 600.055; 600.057; 601.217;ISE | Approved | no | ||
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ADAS @ adas @ SRV2013 | Serial | 2334 | ||
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| Author | Joan Serrat; Jordi Vitria; J. Pladellorens | ||||
| Title | Morphological Segmentation of Heart Scintigraphic image Sequences. | Type | Conference Article | ||
| Year | 1991 | Publication | Computer Assisted Radiology. | Abbreviated Journal | |
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| Address | Berlin | ||||
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| Notes | ADAS;OR;MV | Approved | no | ||
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ADAS @ adas @ SVP1991 | Serial | 263 | ||
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| Author | David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville | ||||
| Title | A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images | Type | Conference Article | ||
| Year | 2017 | Publication | 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Deep Learning; Medical Imaging | ||||
| Abstract | Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation. | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CARS | ||
| Notes | ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 | Approved | no | ||
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ADAS @ adas @ VBS2017a | Serial | 2880 | ||
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| Author | David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin | ||||
| Title | Virtual Worlds and Active Learning for Human Detection | Type | Conference Article | ||
| Year | 2011 | Publication | 13th International Conference on Multimodal Interaction | Abbreviated Journal | |
| Volume | Issue | Pages | 393-400 | ||
| Keywords | Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning | ||||
| Abstract | Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid. | ||||
| Address | Alicante, Spain | ||||
| Corporate Author | Thesis | ||||
| Publisher | ACM DL | Place of Publication | New York, NY, USA, USA | Editor | |
| Language | English | Summary Language | English | Original Title | Virtual Worlds and Active Learning for Human Detection |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4503-0641-6 | Medium | ||
| Area | Expedition | Conference | ICMI | ||
| Notes | ADAS | Approved | yes | ||
| Call Number |
ADAS @ adas @ VLP2011a | Serial | 1683 | ||
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| Author | David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin | ||||
| Title | Cool world: domain adaptation of virtual and real worlds for human detection using active learning | Type | Conference Article | ||
| Year | 2011 | Publication | NIPS Domain Adaptation Workshop: Theory and Application | Abbreviated Journal | NIPS-DA |
| Volume | Issue | Pages | |||
| Keywords | Pedestrian Detection; Virtual; Domain Adaptation; Active Learning | ||||
| Abstract | Image based human detection is of paramount interest for different applications. The most promising human detectors rely on discriminatively learnt classifiers, i.e., trained with labelled samples. However, labelling is a manual intensive task, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, in Marin et al. we have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera and the same type of scenario. Accordingly, in Vazquez et al. we cast the problem as one of supervised domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we use an active learning technique. Thus, ultimately our human model is learnt by the combination of virtual- and real-world labelled samples which, to the best of our knowledge, was not done before. Here, we term such combined space cool world. In this extended abstract we summarize our proposal, and include quantitative results from Vazquez et al. showing its validity. | ||||
| Address | Granada, Spain | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Granada, Spain | Editor | ||
| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference | DA-NIPS | ||
| Notes | ADAS | Approved | no | ||
| Call Number |
ADAS @ adas @ VLP2011b | Serial | 1756 | ||
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| Author | David Vazquez; Antonio Lopez; Daniel Ponsa | ||||
| Title | Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection | Type | Conference Article | ||
| Year | 2012 | Publication | 21st International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 3492 - 3495 | ||
| Keywords | Pedestrian Detection; Domain Adaptation; Virtual worlds | ||||
| Abstract | Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1). | ||||
| Address | Tsukuba Science City, Japan | ||||
| Corporate Author | Thesis | ||||
| Publisher | IEEE | Place of Publication | Tsukuba Science City, JAPAN | Editor | |
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1051-4651 | ISBN | 978-1-4673-2216-4 | Medium | |
| Area | Expedition | Conference | ICPR | ||
| Notes | ADAS | Approved | no | ||
| Call Number |
ADAS @ adas @ VLP2012 | Serial | 1981 | ||
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| Author | David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa | ||||
| Title | Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes | Type | Conference Article | ||
| Year | 2013 | Publication | CVPR Workshop on Ground Truth – What is a good dataset? | Abbreviated Journal | |
| Volume | Issue | Pages | 706 - 711 | ||
| Keywords | Pedestrian Detection; Domain Adaptation | ||||
| Abstract | Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs. | ||||
| Address | Portland; Oregon; June 2013 | ||||
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| Publisher | IEEE | Place of Publication | Editor | ||
| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference | CVPRW | ||
| Notes | ADAS; 600.054; 600.057; 601.217 | Approved | no | ||
| Call Number |
ADAS @ adas @ VXR2013a | Serial | 2219 | ||
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| Author | J. Weickert; Bart M. Ter Haar Romeny; Antonio Lopez; W. Van Enk | ||||
| Title | Orientation Analysis by Coherence-Enhancing Diffusion. | Type | Conference Article | ||
| Year | 1997 | Publication | Proc. of the Real World Computing Symposium. | Abbreviated Journal | |
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| Notes | ADAS | Approved | no | ||
| Call Number |
ADAS @ adas @ WTL1997 | Serial | 211 | ||
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| Author | Jiaolong Xu; Sebastian Ramos; Xu Hu; David Vazquez; Antonio Lopez | ||||
| Title | Multi-task Bilinear Classifiers for Visual Domain Adaptation | Type | Conference Article | ||
| Year | 2013 | Publication | Advances in Neural Information Processing Systems Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Domain Adaptation; Pedestrian Detection; ADAS | ||||
| Abstract | We propose a method that aims to lessen the significant accuracy degradation
that a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation. The bilinear classifier encodes the feature transformation and classification parameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines. |
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| Address | Lake Tahoe; Nevada; USA; December 2013 | ||||
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| Area | Expedition | Conference | NIPSW | ||
| Notes | ADAS; 600.054; 600.057; 601.217;ISE | Approved | no | ||
| Call Number |
ADAS @ adas @ XRH2013 | Serial | 2340 | ||
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| Author | Jiaolong Xu; Sebastian Ramos;David Vazquez; Antonio Lopez | ||||
| Title | Cost-sensitive Structured SVM for Multi-category Domain Adaptation | Type | Conference Article | ||
| Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 3886 - 3891 | ||
| Keywords | Domain Adaptation; Pedestrian Detection | ||||
| Abstract | Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition. | ||||
| Address | Stockholm; Sweden; August 2014 | ||||
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| Publisher | IEEE | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1051-4651 | ISBN | Medium | ||
| Area | Expedition | Conference | ICPR | ||
| Notes | ADAS; 600.057; 600.054; 601.217; 600.076 | Approved | no | ||
| Call Number |
ADAS @ adas @ XRV2014a | Serial | 2434 | ||
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| Author | M.J. Yzuel; J. Pladellorens; Joan Serrat; A. Dupuy | ||||
| Title | Application restauration and edge detection techniques in the calculation of left ventricular volumes. | Type | Conference Article | ||
| Year | 1993 | Publication | Optics in Medicine, Biology and Environmental Research : Selected contributions to the first International Conference on Optics within Life Sciences (OWLS I) | Abbreviated Journal | |
| Volume | Issue | Pages | 374-375 | ||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | ADAS | Approved | no | ||
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ADAS @ adas @ YPS1993 | Serial | 244 | ||
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| Author | Marçal Rusiñol; V. Poulain d'Andecy; Dimosthenis Karatzas; Josep Llados | ||||
| Title | Classification of Administrative Document Images by Logo Identification | Type | Conference Article | ||
| Year | 2013 | Publication | 10th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
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| Abstract | This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier's graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents. | ||||
| Address | Bethlehem; PA; USA; August 2013 | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.056; 600.045; 605.203 | Approved | no | ||
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Admin @ si @ | Serial | 2348 | ||
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| Author | Adriana Romero; Simeon Petkov; Carlo Gatta; M.Sabate; Petia Radeva | ||||
| Title | Efficient automatic segmentation of vessels | Type | Conference Article | ||
| Year | 2012 | Publication | 16th Conference on Medical Image Understanding and Analysis | Abbreviated Journal | |
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| Address | Swansea, United Kingdom | ||||
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| Area | Expedition | Conference | MIUA | ||
| Notes | MILAB | Approved | no | ||
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Admin @ si @ | Serial | 2137 | ||
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| Author | Pau Riba; Alicia Fornes; Josep Llados | ||||
| Title | Towards the Alignment of Handwritten Music Scores | Type | Conference Article | ||
| Year | 2015 | Publication | 11th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
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| Abstract | It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study. This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such differences. Given the difficulties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the staff lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results. | ||||
| Address | Nancy; France; August 2015 | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | Bart Lamiroy; Rafael Dueire Lins | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-319-52158-9 | Medium | ||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number |
Admin @ si @ | Serial | 2874 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Learning to Colorize Infrared Images | Type | Conference Article | ||
| Year | 2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent System | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | CNN in multispectral imaging; Image colorization | ||||
| Abstract | This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very di erent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach. | ||||
| Address | Porto; Portugal; June 2017 | ||||
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| Area | Expedition | Conference | PAAMS | ||
| Notes | ADAS; MSIAU; 600.086; 600.122; 600.118 | Approved | no | ||
| Call Number |
Admin @ si @ | Serial | 2919 | ||
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