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Author Monica Piñol; Angel Sappa; Ricardo Toledo edit  doi
openurl 
  Title (up) Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy Type Journal Article
  Year 2015 Publication Neurocomputing Abbreviated Journal NEUCOM  
  Volume 150 Issue A Pages 106–115  
  Keywords Reinforcement learning; Q-learning; Bag of features; Descriptors  
  Abstract This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach.  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.055; 600.076 Approved no  
  Call Number Admin @ si @ PST2015 Serial 2473  
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Author Angel Sappa; Fadi Dornaika; Daniel Ponsa; David Geronimo; Antonio Lopez edit   pdf
url  openurl
  Title (up) An Efficient Approach to Onboard Stereo Vision System Pose Estimation Type Journal
  Year 2008 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 9 Issue 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  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  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
doi  openurl
  Title (up) 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 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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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
doi  openurl
  Title (up) An Iterative Multiresolution Scheme for SFM with Missing Data: single and multiple object scenes Type Journal Article
  Year 2010 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 28 Issue 1 Pages 164-176  
  Keywords  
  Abstract Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0262-8856 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2010 Serial 1278  
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Author Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate edit  url
doi  openurl
  Title (up) An overview of incremental feature extraction methods based on linear subspaces Type Journal Article
  Year 2018 Publication Knowledge-Based Systems Abbreviated Journal KBS  
  Volume 145 Issue Pages 219-235  
  Keywords  
  Abstract With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0950-7051 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ DFH2018 Serial 3090  
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Author A.F. Sole; S. Ngan; G. Sapiro; X. Hu; Antonio Lopez edit   pdf
openurl 
  Title (up) Anisotropic 2-D and 3-D Averaging of fMRI Signals Type Journal
  Year 2001 Publication IEEE Transactions on Medical Imaging, 20(2): 86–93 (IF: 3.142) Abbreviated Journal  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ SNS2001 Serial 165  
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Author Carme Julia; Angel Sappa; Felipe Lumbreras edit  openurl
  Title (up) Aprendiendo a recrear la realidad en 3D Type Journal
  Year 2008 Publication UAB Divulga, Revista de divulgacion cientifica Abbreviated Journal  
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  Notes spreading;ADAS Approved no  
  Call Number ADAS @ adas @ JSL2008b Serial 1472  
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Author Joan Marc Llargues Asensio; Juan Peralta; Raul Arrabales; Manuel Gonzalez Bedia; Paulo Cortez; Antonio Lopez edit  doi
openurl 
  Title (up) Artificial Intelligence Approaches for the Generation and Assessment of Believable Human-Like Behaviour in Virtual Characters Type Journal Article
  Year 2014 Publication Expert Systems With Applications Abbreviated Journal EXSY  
  Volume 41 Issue 16 Pages 7281–7290  
  Keywords Turing test; Human-like behaviour; Believability; Non-player characters; Cognitive architectures; Genetic algorithm; Artificial neural networks  
  Abstract Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA–CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.  
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  Notes ADAS; 600.055; 600.057; 600.076 Approved no  
  Call Number Admin @ si @ LPA2014 Serial 2500  
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Author Marçal Rusiñol; J. Chazalon; Katerine Diaz edit   pdf
doi  openurl
  Title (up) Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness Type Journal Article
  Year 2018 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 77 Issue 11 Pages 13773-13798  
  Keywords Augmented reality; Document image matching; Educational applications  
  Abstract This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here.  
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  Notes DAG; ADAS; 600.084; 600.121; 600.118 Approved no  
  Call Number Admin @ si @ RCD2018 Serial 2996  
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Author Jaume Amores; N. Sebe; Petia Radeva edit  doi
openurl 
  Title (up) 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 3 Pages 201–209  
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  Notes ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ ASR2006 Serial 643  
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