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Author Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin edit   pdf
url  openurl
  Title Mathematics learning opportunities when playing a Tower Defense Game Type Journal
  Year 2015 Publication International Journal of Serious Games Abbreviated Journal IJSG  
  Volume 2 Issue (down) 4 Pages 57-71  
  Keywords Tower Defense game; learning opportunities; mathematics; problem solving; game design  
  Abstract A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes.  
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  Area Expedition Conference  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ HJG2015 Serial 2730  
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Author Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo edit   pdf
doi  openurl
  Title Cross-Spectral Local Descriptors via Quadruplet Network Type Journal Article
  Year 2017 Publication Sensors Abbreviated Journal SENS  
  Volume 17 Issue (down) 4 Pages 873  
  Keywords  
  Abstract This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.  
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  Notes ADAS; 600.086; 600.118 Approved no  
  Call Number Admin @ si @ ASA2017 Serial 2914  
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Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri edit   pdf
doi  openurl
  Title Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction Type Journal Article
  Year 2018 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 60 Issue (down) 4 Pages 512-524  
  Keywords  
  Abstract This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model.
 
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  Notes DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129 Approved no  
  Call Number Admin @ si @ DMH2018a Serial 3062  
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Author Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez edit  url
doi  openurl
  Title Semantic Monocular Depth Estimation Based on Artificial Intelligence Type Journal Article
  Year 2020 Publication IEEE Intelligent Transportation Systems Magazine Abbreviated Journal ITSM  
  Volume 13 Issue (down) 4 Pages 99-103  
  Keywords  
  Abstract Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation.  
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  Notes ADAS; 600.124; 600.118 Approved no  
  Call Number Admin @ si @ GUH2019 Serial 3306  
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Author Arnau Ramisa; Alex Goldhoorn; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras edit  doi
openurl 
  Title Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas Type Journal Article
  Year 2011 Publication Journal of Intelligent and Robotic Systems Abbreviated Journal JIRC  
  Volume 64 Issue (down) 3-4 Pages 625-649  
  Keywords  
  Abstract Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments.  
  Address  
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  Publisher Springer Netherlands Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0921-0296 ISBN Medium  
  Area Expedition Conference  
  Notes RV;ADAS Approved no  
  Call Number Admin @ si @ RGA2011 Serial 1728  
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Author J. Pladellorens; M.J. Yzuel; J. Castell; Joan Serrat edit  openurl
  Title Calculo automatico del volumen del ventriculo izquierdo. Comparacion con expertos. Type Journal
  Year 1993 Publication Optica Pura y Aplicada. Abbreviated Journal  
  Volume 26 Issue (down) 3 Pages 685–691  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ PYC1993 Serial 149  
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Author Antonio Lopez; Ernest Valveny; Juan J. Villanueva edit  url
openurl 
  Title Real-time quality control of surgical material packaging by artificial vision Type Journal Article
  Year 2005 Publication Assembly Automation Abbreviated Journal  
  Volume 25 Issue (down) 3 Pages  
  Keywords  
  Abstract IF: 0.061)  
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  Notes ADAS;DAG Approved no  
  Call Number ADAS @ adas @ LVV2005 Serial 552  
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Author Jaume Amores; N. Sebe; Petia Radeva edit  doi
openurl 
  Title Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier Type Journal Article
  Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 27 Issue (down) 3 Pages 201–209  
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  Notes ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ ASR2006 Serial 643  
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Author Angel Sappa; Fadi Dornaika; Daniel Ponsa; David Geronimo; Antonio Lopez edit   pdf
url  openurl
  Title An Efficient Approach to Onboard Stereo Vision System Pose Estimation Type Journal Article
  Year 2008 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 9 Issue (down) 3 Pages 476–490  
  Keywords Camera extrinsic parameter estimation, ground plane estimation, onboard stereo vision system  
  Abstract This paper presents an efficient technique for estimating the pose of an onboard stereo vision system relative to the environment’s dominant surface area, which is supposed to be the road surface. Unlike previous approaches, it can be used either for urban or highway scenarios since it is not based on a specific visual traffic feature extraction but on 3-D raw data points. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact 2-D representation of the original 3-D data points is computed. Then, a RANdom SAmple Consensus (RANSAC) based least-squares approach is used to fit a plane to the road. Fast RANSAC fitting is obtained by selecting points according to a probability function that takes into account the density of points at a given depth. Finally, stereo camera height and pitch angle are computed related to the fitted road plane. The proposed technique is intended to be used in driverassistance systems for applications such as vehicle or pedestrian detection. Experimental results on urban environments, which are the most challenging scenarios (i.e., flat/uphill/downhill driving, speed bumps, and car’s accelerations), are presented. These results are validated with manually annotated ground truth. Additionally, comparisons with previous works are presented to show the improvements in the central processing unit processing time, as well as in the accuracy of the obtained results.  
  Address  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Editor  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ SDP2008 Serial 1000  
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
url  openurl
  Title An iterative multiresolution scheme for SFM with missing data Type Journal Article
  Year 2009 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 34 Issue (down) 3 Pages 240–258  
  Keywords  
  Abstract Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach.
 
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2009a Serial 1163  
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