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Author Patricia Marquez; Debora Gil; R.Mester; Aura Hernandez-Sabate edit   pdf
openurl 
  Title Local Analysis of Confidence Measures for Optical Flow Quality Evaluation Type Conference Article
  Year 2014 Publication 9th International Conference on Computer Vision Theory and Applications Abbreviated Journal  
  Volume 3 Issue Pages 450-457  
  Keywords Optical Flow; Confidence Measure; Performance Evaluation.  
  Abstract Optical Flow (OF) techniques facing the complexity of real sequences have been developed in the last years. Even using the most appropriate technique for our specific problem, at some points the output flow might fail to achieve the minimum error required for the system. Confidence measures computed from either input data or OF output should discard those points where OF is not accurate enough for its further use. It follows that evaluating the capabilities of a confidence measure for bounding OF error is as important as the definition
itself. In this paper we analyze different confidence measures and point out their advantages and limitations for their use in real world settings. We also explore the agreement with current tools for their evaluation of confidence measures performance.		  
  Address (up) Lisboa; January 2014  
  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 VISAPP  
  Notes IAM; ADAS; 600.044; 600.060; 600.057; 601.145; 600.076; 600.075 Approved no  
  Call Number Admin @ si @ MGM2014 Serial 2432  
Permanent link to this record
 
 
Author Q. Xue; Laura Igual; A. Berenguel; M. Guerrieri; L. Garrido edit   pdf
openurl 
  Title Active Contour Segmentation with Affine Coordinate-Based Parametrization Type Conference Article
  Year 2014 Publication 9th International Conference on Computer Vision Theory and Applications Abbreviated Journal  
  Volume 1 Issue Pages 5-14  
  Keywords Active Contours; Affine Coordinates; Mean Value Coordinates  
  Abstract In this paper, we present a new framework for image segmentation based on parametrized active contours. The contour and the points of the image space are parametrized using a set of reduced control points that have to form a closed polygon in two dimensional problems and a closed surface in three dimensional problems. By moving the control points, the active contour evolves. We use mean value coordinates as the parametrization tool for the interface, which allows to parametrize any point of the space, inside or outside the closed polygon
or surface. Region-based energies such as the one proposed by Chan and Vese can be easily implemented in both two and three dimensional segmentation problems. We show the usefulness of our approach with several experiments.		  
  Address (up) Lisboa; January 2014  
  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 VISAPP  
  Notes OR;MILAB Approved no  
  Call Number Admin @ si @ XIB2014 Serial 2452  
Permanent link to this record
 
 
Author P. Ricaurte; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa edit   pdf
doi  openurl
  Title Performance Evaluation of Feature Point Descriptors in the Infrared Domain Type Conference Article
  Year 2014 Publication 9th International Conference on Computer Vision Theory and Applications Abbreviated Journal  
  Volume 1 Issue Pages 545-550  
  Keywords Infrared Imaging; Feature Point Descriptors  
  Abstract This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered.  
  Address (up) Lisboa; Portugal; January 2014  
  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 VISAPP  
  Notes ADAS; 600.055; 600.076 Approved no  
  Call Number Admin @ si @ RCA2014b Serial 2476  
Permanent link to this record
 
 
Author Naveen Onkarappa; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa edit   pdf
doi  openurl
  Title Cross-spectral Stereo Correspondence using Dense Flow Fields Type Conference Article
  Year 2014 Publication 9th International Conference on Computer Vision Theory and Applications Abbreviated Journal  
  Volume 3 Issue Pages 613-617  
  Keywords Cross-spectral Stereo Correspondence; Dense Optical Flow; Infrared and Visible Spectrum  
  Abstract This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach.  
  Address (up) Lisboa; Portugal; January 2014  
  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 VISAPP  
  Notes ADAS; 600.055; 600.076 Approved no  
  Call Number Admin @ si @ OAV2014 Serial 2477  
Permanent link to this record
 
 
Author Ariel Amato; Felipe Lumbreras; Angel Sappa edit   pdf
openurl 
  Title A General-purpose Crowdsourcing Platform for Mobile Devices Type Conference Article
  Year 2014 Publication 9th International Conference on Computer Vision Theory and Applications Abbreviated Journal  
  Volume 3 Issue Pages 211-215  
  Keywords Crowdsourcing Platform; Mobile Crowdsourcing  
  Abstract This paper presents details of a general purpose micro-task on-demand platform based on the crowdsourcing philosophy. This platform was specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquity and iii) embedded sensors. The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks. Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and tasksolver). Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way. Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications. Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform.  
  Address (up) Lisboa; Portugal; January 2014  
  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 VISAPP  
  Notes ISE; ADAS; 600.054; 600.055; 600.076; 600.078 Approved no  
  Call Number Admin @ si @ ALS2014 Serial 2478  
Permanent link to this record
 
 
Author Alicia Fornes; Josep Llados; Joan Mas; Joana Maria Pujadas-Mora; Anna Cabre edit   pdf
doi  isbn
openurl 
  Title A Bimodal Crowdsourcing Platform for Demographic Historical Manuscripts Type Conference Article
  Year 2014 Publication Digital Access to Textual Cultural Heritage Conference Abbreviated Journal  
  Volume Issue Pages 103-108  
  Keywords  
  Abstract In this paper we present a crowdsourcing web-based application for extracting information from demographic handwritten document images. The proposed application integrates two points of view: the semantic information for demographic research, and the ground-truthing for document analysis research. Concretely, the application has the contents view, where the information is recorded into forms, and the labeling view, with the word labels for evaluating document analysis techniques. The crowdsourcing architecture allows to accelerate the information extraction (many users can work simultaneously), validate the information, and easily provide feedback to the users. We finally show how the proposed application can be extended to other kind of demographic historical manuscripts.  
  Address (up) Madrid; May 2014  
  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 978-1-4503-2588-2 Medium  
  Area Expedition Conference DATeCH  
  Notes DAG; 600.061; 602.006; 600.077 Approved no  
  Call Number Admin @ si @ FLM2014 Serial 2516  
Permanent link to this record
 
 
Author B. Zhou; Agata Lapedriza; J. Xiao; A. Torralba; A. Oliva edit  url
openurl 
  Title Learning Deep Features for Scene Recognition using Places Database Type Conference Article
  Year 2014 Publication 28th Annual Conference on Neural Information Processing Systems Abbreviated Journal  
  Volume Issue Pages 487-495  
  Keywords  
  Abstract  
  Address (up) Montreal; Canada; December 2014  
  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 NIPS  
  Notes OR;MV Approved no  
  Call Number Admin @ si @ ZLX2014 Serial 2621  
Permanent link to this record
 
 
Author Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier edit  openurl
  Title Normalisation et validation d'images de documents capturées en mobilité Type Conference Article
  Year 2014 Publication Colloque International Francophone sur l'Écrit et le Document Abbreviated Journal  
  Volume Issue Pages 109-124  
  Keywords mobile document image acquisition; perspective correction; illumination correction; quality assessment; focus measure; OCR accuracy prediction  
  Abstract Mobile document image acquisition integrates many distortions which must be corrected or detected on the device, before the document becomes unavailable or paying data transmission fees. In this paper, we propose a system to correct perspective and illumination issues, and estimate the sharpness of the image for OCR recognition. The correction step relies on fast and accurate border detection followed by illumination normalization. Its evaluation on a private dataset shows a clear improvement on OCR accuracy. The quality assessment
step relies on a combination of focus measures. Its evaluation on a public dataset shows that this simple method compares well to state of the art, learning-based methods which cannot be embedded on a mobile, and outperforms metric-based methods.		  
  Address (up) Nancy; France; March 2014  
  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 CIFED  
  Notes DAG; 601.223; 600.077 Approved no  
  Call Number Admin @ si @ RCO2014b Serial 2546  
Permanent link to this record
 
 
Author Christophe Rigaud; Clement Guerin edit  openurl
  Title Localisation contextuelle des personnages de bandes dessinées Type Conference Article
  Year 2014 Publication Colloque International Francophone sur l'Écrit et le Document Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Les auteurs proposent une méthode de localisation des personnages dans des cases de bandes dessinées en s'appuyant sur les caractéristiques des bulles de dialogue. L'évaluation montre un taux de localisation des personnages allant jusqu'à 65%.  
  Address (up) Nancy; Francia; March 2014  
  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 CIFED  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ RiG2014 Serial 2481  
Permanent link to this record
 
 
Author P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes edit   pdf
openurl 
  Title Représentation par graphe de mots manuscrits dans les images pour la recherche par similarité Type Conference Article
  Year 2014 Publication Colloque International Francophone sur l'Écrit et le Document Abbreviated Journal  
  Volume Issue Pages 233-248  
  Keywords word spotting; graph-based representation; shape context description; graph edit distance; DTW; block merging; query by example  
  Abstract Effective information retrieval on handwritten document images has always been
a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labeled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment results introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods.		  
  Address (up) Nancy; Francia; March 2014  
  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 CIFED  
  Notes DAG; 600.061; 602.006; 600.077 Approved no  
  Call Number Admin @ si @ WEG2014c Serial 2564  
Permanent link to this record
 
 
Author Antonio Hernandez; Stan Sclaroff; Sergio Escalera edit   pdf
doi  openurl
  Title Contextual rescoring for Human Pose Estimation Type Conference Article
  Year 2014 Publication 25th British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract A contextual rescoring method is proposed for improving the detection of body joints of a pictorial structure model for human pose estimation. A set of mid-level parts is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body joint hypotheses. A technique is proposed for the automatic discovery of a compact subset of poselets that covers a set of validation images
while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for body joint detections, given its relationship to detections of other body joints and mid-level parts in the image. This new score complements the unary potential of a discriminatively trained pictorial structure model. Experiments on two benchmarks show performance improvements when considering the proposed mid-level image representation and rescoring approach in comparison with other pictorial structure-based approaches.		  
  Address (up) Nottingham; UK; September 2013  
  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 BMVC  
  Notes HuPBA;MILAB Approved no  
  Call Number HSE2014 Serial 2525  
Permanent link to this record
 
 
Author Adria Ruiz; Joost Van de Weijer; Xavier Binefa edit   pdf
url  openurl
  Title Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization Type Conference Article
  Year 2014 Publication 25th British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection.  
  Address (up) Nottingham; UK; September 2014  
  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 BMVC  
  Notes LAMP; CIC; 600.074; 600.079 Approved no  
  Call Number Admin @ si @ RWB2014 Serial 2508  
Permanent link to this record
 
 
Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title Incremental Domain Adaptation of Deformable Part-based Models Type Conference Article
  Year 2014 Publication 25th British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords Pedestrian Detection; Part-based models; Domain Adaptation  
  Abstract Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data.		  
  Address (up) Nottingham; uk; September 2014  
  Corporate Author Thesis  
  Publisher BMVA Press Place of Publication Editor Valstar, Michel and French, Andrew and Pridmore, Tony  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference BMVC  
  Notes ADAS; 600.057; 600.054; 600.076 Approved no  
  Call Number XRV2014c; ADAS @ adas @ xrv2014c Serial 2455  
Permanent link to this record
 
 
Author M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer edit   pdf
doi  openurl
  Title Adaptive color attributes for real-time visual tracking Type Conference Article
  Year 2014 Publication 27th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 1090 - 1097  
  Keywords  
  Abstract Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally
efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.
This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional
variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms
state-of-the-art tracking methods while running at more than 100 frames per second.		  
  Address (up) Nottingham; UK; September 2014  
  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 CIC; LAMP; 600.074; 600.079 Approved no  
  Call Number Admin @ si @ DKF2014 Serial 2509  
Permanent link to this record
 
 
Author Oualid M. Benkarim; Petia Radeva; Laura Igual edit   pdf
doi  isbn
openurl 
  Title Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation Type Conference Article
  Year 2014 Publication 8th Conference on Articulated Motion and Deformable Objects Abbreviated Journal  
  Volume 8563 Issue Pages 138-147  
  Keywords MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classi cation  
  Abstract The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems.		  
  Address (up) Palma de Mallorca; July 2014  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-319-08848-8 Medium  
  Area Expedition Conference AMDO  
  Notes MILAB; OR Approved no  
  Call Number Admin @ si @ BRI2014 Serial 2494  
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