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Author David Vazquez; Antonio Lopez; Daniel Ponsa edit   pdf
isbn  openurl
  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 (down) Tsukuba Science City, JAPAN Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  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 Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers edit   pdf
openurl 
  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 (down) Sydney, Australy Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICCVW-VisDA  
  Notes ADAS; 600.054; 600.055; 600.057; 601.217;ISE Approved no  
  Call Number ADAS @ adas @ SRV2013 Serial 2334  
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Author David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin edit   pdf
doi  isbn
openurl 
  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 (down) 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 Geronimo; Frederic Lerasle; Antonio Lopez edit   pdf
openurl 
  Title State-driven particle filter for multi-person tracking Type Conference Article
  Year 2012 Publication 11th International Conference on Advanced Concepts for Intelligent Vision Systems Abbreviated Journal  
  Volume 7517 Issue Pages 467-478  
  Keywords human tracking  
  Abstract Multi-person tracking can be exploited in applications such as driver assistance, surveillance, multimedia and human-robot interaction. With the help of human detectors, particle filters offer a robust method able to filter noisy detections and provide temporal coherence. However, some traditional problems such as occlusions with other targets or the scene, temporal drifting or even the lost targets detection are rarely considered, making the systems performance decrease. Some authors propose to overcome these problems using heuristics not explained
and formalized in the papers, for instance by defining exceptions to the model updating depending on tracks overlapping. In this paper we propose to formalize these events by the use of a state-graph, defining the current state of the track (e.g., potential , tracked, occluded or lost) and the transitions between states in an explicit way. This approach has the advantage of linking track actions such as the online underlying models updating, which gives flexibility to the system. It provides an explicit representation to adapt the multiple parallel trackers depending on the context, i.e., each track can make use of a specific filtering strategy, dynamic model, number of particles, etc. depending on its state. We implement this technique in a single-camera multi-person tracker and test
it in public video sequences.		  
  Address Brno, Chzech Republic  
  Corporate Author Thesis  
  Publisher Springer Place of Publication (down) Heidelberg Editor J. Blanc-Talon et al.  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ACIVS  
  Notes ADAS Approved yes  
  Call Number GLL2012; ADAS @ adas @ gll2012a Serial 1990  
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Author David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin edit   pdf
openurl 
  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 (down) Granada, Spain Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference DA-NIPS  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ VLP2011b Serial 1756  
Permanent link to this record
 
 
Author Patricia Marquez;Debora Gil;Aura Hernandez-Sabate edit   pdf
doi  isbn
openurl 
  Title A Complete Confidence Framework for Optical Flow Type Conference Article
  Year 2012 Publication 12th European Conference on Computer Vision – Workshops and Demonstrations Abbreviated Journal  
  Volume 7584 Issue 2 Pages 124-133  
  Keywords Optical flow, confidence measures, sparsification plots, error prediction plots  
  Abstract Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations.  
  Address  
  Corporate Author Thesis  
  Publisher Springer-Verlag Place of Publication (down) Florence, Italy, October 7-13, 2012 Editor Andrea Fusiello, Vittorio Murino ,Rita Cucchiara  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-642-33867-0 Medium  
  Area Expedition Conference ECCVW  
  Notes IAM;ADAS; Approved no  
  Call Number IAM @ iam @ MGH2012b Serial 1991  
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Author Muhammad Anwer Rao; David Vazquez; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Opponent Colors for Human Detection Type Conference Article
  Year 2011 Publication 5th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume 6669 Issue Pages 363-370  
  Keywords Pedestrian Detection; Color; Part Based Models  
  Abstract Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper.  
  Address Las Palmas de Gran Canaria. Spain  
  Corporate Author Thesis  
  Publisher Springer Place of Publication (down) Berlin Heidelberg Editor J. Vitria; J.M. Sanches; M. Hernandez  
  Language English Summary Language English Original Title Opponent Colors for Human Detection  
  Series Editor Series Title Lecture Notes on Computer Science Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-21256-7 Medium  
  Area Expedition Conference IbPRIA  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ RVL2011a Serial 1666  
Permanent link to this record
 
 
Author Muhammad Anwer Rao; David Vazquez; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Color Contribution to Part-Based Person Detection in Different Types of Scenarios Type Conference Article
  Year 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 6855 Issue II Pages 463-470  
  Keywords Pedestrian Detection; Color  
  Abstract Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution.  
  Address Seville, Spain  
  Corporate Author Thesis  
  Publisher Springer Place of Publication (down) Berlin Heidelberg Editor P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch  
  Language English Summary Language english Original Title Color Contribution to Part-Based Person Detection in Different Types of Scenarios  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-23677-8 Medium  
  Area Expedition Conference CAIP  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ RVL2011b Serial 1665  
Permanent link to this record
 
 
Author Aura Hernandez-Sabate; Debora Gil; David Roche; Monica M. S. Matsumoto; Sergio S. Furuie edit   pdf
url  openurl
  Title Inferring the Performance of Medical Imaging Algorithms Type Conference Article
  Year 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 6854 Issue Pages 520-528  
  Keywords Validation, Statistical Inference, Medical Imaging Algorithms.  
  Abstract Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.		  
  Address Sevilla  
  Corporate Author Thesis  
  Publisher Springer-Verlag Berlin Heidelberg Place of Publication (down) Berlin Editor Pedro Real; Daniel Diaz-Pernil; Helena Molina-Abril; Ainhoa Berciano; Walter Kropatsch  
  Language Summary Language Original Title  
  Series Editor Series Title L Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CAIP  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ HGR2011 Serial 1676  
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Author Marçal Rusiñol; David Aldavert; Dimosthenis Karatzas; Ricardo Toledo; Josep Llados edit  doi
isbn  openurl
  Title Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval Type Conference Article
  Year 2011 Publication 33rd European Conference on Information Retrieval Abbreviated Journal  
  Volume 6611 Issue Pages 314-325  
  Keywords  
  Abstract In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset.  
  Address Dublin, Ireland  
  Corporate Author Thesis  
  Publisher Springer Place of Publication (down) Berlin Editor P. Clough; C. Foley; C. Gurrin; G.J.F. Jones; W. Kraaij; H. Lee; V. Murdoch  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-642-20160-8 Medium  
  Area Expedition Conference ECIR  
  Notes DAG; RV;ADAS Approved no  
  Call Number Admin @ si @ RAK2011 Serial 1737  
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