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Author Monica Piñol; Angel Sappa; Angeles Lopez; Ricardo Toledo edit   pdf
openurl 
  Title Feature Selection Based on Reinforcement Learning for Object Recognition Type Conference Article
  Year 2012 Publication Adaptive Learning Agents Workshop Abbreviated Journal  
  Volume Issue Pages 33-39  
  Keywords  
  Abstract  
  Address Valencia  
  Corporate Author Thesis  
  Publisher (up) 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 ALA  
  Notes ADAS; RV Approved no  
  Call Number Admin @ si @ PSL2012 Serial 2018  
Permanent link to this record
 
 
Author German Ros; Angel Sappa; Daniel Ponsa; Antonio Lopez edit   pdf
openurl 
  Title Visual SLAM for Driverless Cars: A Brief Survey Type Conference Article
  Year 2012 Publication IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles Abbreviated Journal  
  Volume Issue Pages  
  Keywords SLAM  
  Abstract  
  Address Alcalá de Henares  
  Corporate Author Thesis  
  Publisher (up) 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 IVW  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RSP2012; ADAS @ adas Serial 2019  
Permanent link to this record
 
 
Author German Ros; Jesus Martinez del Rincon; Gines Garcia-Mateos edit   pdf
url  isbn
openurl 
  Title Articulated Particle Filter for Hand Tracking Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 3581 - 3585  
  Keywords  
  Abstract This paper proposes a new version of Particle Filter, called Articulated Particle Filter – ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper.  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher (up) Place of Publication 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 Admin @ si @ RMG2012 Serial 2031  
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Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez edit   pdf
openurl 
  Title Multiple target tracking and identity linking under split, merge and occlusion of targets and observations Type Conference Article
  Year 2012 Publication 1st International Conference on Pattern Recognition Applications and Methods Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Algarve, Portugal  
  Corporate Author Thesis  
  Publisher (up) 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 ICPRAM  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RSL2012c; ADAS @ adas Serial 2034  
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Author Ferran Diego; G.D. Evangelidis; Joan Serrat edit   pdf
url  openurl
  Title Night-time outdoor surveillance by mobile cameras Type Conference Article
  Year 2012 Publication 1st International Conference on Pattern Recognition Applications and Methods Abbreviated Journal  
  Volume 2 Issue Pages 365-371  
  Keywords  
  Abstract This paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be “localized” in former sequences and registered with the current frames. To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods.  
  Address Algarve, Portugal  
  Corporate Author Thesis  
  Publisher (up) 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 ICPRAM  
  Notes ADAS Approved no  
  Call Number Admin @ si @ DES2012 Serial 2035  
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Author German Ros; J. Guerrero; Angel Sappa; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title VSLAM pose initialization via Lie groups and Lie algebras optimization Type Conference Article
  Year 2013 Publication Proceedings of IEEE International Conference on Robotics and Automation Abbreviated Journal  
  Volume Issue Pages 5740 - 5747  
  Keywords SLAM  
  Abstract We present a novel technique for estimating initial 3D poses in the context of localization and Visual SLAM problems. The presented approach can deal with noise, outliers and a large amount of input data and still performs in real time in a standard CPU. Our method produces solutions with an accuracy comparable to those produced by RANSAC but can be much faster when the percentage of outliers is high or for large amounts of input data. On the current work we propose to formulate the pose estimation as an optimization problem on Lie groups, considering their manifold structure as well as their associated Lie algebras. This allows us to perform a fast and simple optimization at the same time that conserve all the constraints imposed by the Lie group SE(3). Additionally, we present several key design concepts related with the cost function and its Jacobian; aspects that are critical for the good performance of the algorithm.  
  Address Karlsruhe; Germany; May 2013  
  Corporate Author Thesis  
  Publisher (up) Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1050-4729 ISBN 978-1-4673-5641-1 Medium  
  Area Expedition Conference ICRA  
  Notes ADAS; 600.054; 600.055; 600.057 Approved no  
  Call Number Admin @ si @ RGS2013a; ADAS @ adas @ Serial 2225  
Permanent link to this record
 
 
Author David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados edit   pdf
doi  openurl
  Title Integrating Visual and Textual Cues for Query-by-String Word Spotting Type Conference Article
  Year 2013 Publication 12th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 511 - 515  
  Keywords  
  Abstract In this paper, we present a word spotting framework that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character $n$-grams while the visual one is based on the bag-of-visual-words scheme. These two representations are merged together and projected to a sub-vector space. This transform allows to, given a textual query, retrieve word instances that were only represented by the visual modality. Moreover, this statistical representation can be used together with state-of-the-art indexation structures in order to deal with large-scale scenarios. The proposed method is evaluated using a collection of historical documents outperforming state-of-the-art performances.  
  Address Washington; USA; August 2013  
  Corporate Author Thesis  
  Publisher (up) Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1520-5363 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; ADAS; 600.045; 600.055; 600.061 Approved no  
  Call Number Admin @ si @ ART2013 Serial 2224  
Permanent link to this record
 
 
Author Miguel Oliveira; V.Santos; Angel Sappa edit  openurl
  Title Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition Type Conference Article
  Year 2012 Publication IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Algarve; Portugal  
  Corporate Author Thesis  
  Publisher (up) 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 PPNIV  
  Notes ADAS Approved no  
  Call Number Admin @ si @ OSS2012c Serial 2159  
Permanent link to this record
 
 
Author Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool edit   pdf
doi  openurl
  Title Active MAP Inference in CRFs for Efficient Semantic Segmentation Type Conference Article
  Year 2013 Publication 15th IEEE International Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages 2312 - 2319  
  Keywords Semantic Segmentation  
  Abstract Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains.  
  Address Sydney; Australia; December 2013  
  Corporate Author Thesis  
  Publisher (up) Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1550-5499 ISBN Medium  
  Area Expedition Conference ICCV  
  Notes ADAS; 600.057 Approved no  
  Call Number ADAS @ adas @ RBN2013 Serial 2377  
Permanent link to this record
 
 
Author Jiaolong Xu; David Vazquez; Sebastian Ramos; Antonio Lopez; Daniel Ponsa edit   pdf
doi  openurl
  Title Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers Type Conference Article
  Year 2013 Publication CVPR Workshop on Ground Truth – What is a good dataset? Abbreviated Journal  
  Volume Issue Pages 688 - 693  
  Keywords Pedestrian Detection; Domain Adaptation  
  Abstract Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%.  
  Address Portland; oregon; June 2013  
  Corporate Author Thesis  
  Publisher (up) Place of Publication 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 CVPRW  
  Notes ADAS; 600.054; 600.057; 601.217 Approved yes  
  Call Number XVR2013; ADAS @ adas @ xvr2013a Serial 2220  
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