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Author Joan Marc Llargues Asensio; Juan Peralta; Raul Arrabales; Manuel Gonzalez Bedia; Paulo Cortez; Antonio Lopez edit  doi
openurl 
  Title 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 (up) 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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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.055; 600.057; 600.076 Approved no  
  Call Number Admin @ si @ LPA2014 Serial 2500  
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Author J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin edit  url
doi  openurl
  Title Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm Type Journal Article
  Year 2013 Publication Expert Systems with Applications Abbreviated Journal EXWA  
  Volume 40 Issue (up) 17 Pages 6707-6712  
  Keywords Neural gas; Expert vision; Eye-tracking; Fixations  
  Abstract Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0957-4174 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ CRM2013 Serial 2438  
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Author Jaume Amores edit   pdf
doi  openurl
  Title MILDE: multiple instance learning by discriminative embedding Type Journal Article
  Year 2015 Publication Knowledge and Information Systems Abbreviated Journal KAIS  
  Volume 42 Issue (up) 2 Pages 381-407  
  Keywords Multi-instance learning; Codebook; Bag of words  
  Abstract While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data.  
  Address  
  Corporate Author Thesis  
  Publisher Springer London Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0219-1377 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 601.042; 600.057; 600.076 Approved no  
  Call Number Admin @ si @ Amo2015 Serial 2383  
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
doi  openurl
  Title Rank Estimation in Missing Data Matrix Problems Type Journal Article
  Year 2011 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 39 Issue (up) 2 Pages 140-160  
  Keywords  
  Abstract A novel technique for missing data matrix rank estimation is presented. It is focused on matrices of trajectories, where every element of the matrix corresponds to an image coordinate from a feature point of a rigid moving object at a given frame; missing data are represented as empty entries. The objective of the proposed approach is to estimate the rank of a missing data matrix in order to fill in empty entries with some matrix completion method, without using or assuming neither the number of objects contained in the scene nor the kind of their motion. The key point of the proposed technique consists in studying the frequency behaviour of the individual trajectories, which are seen as 1D signals. The main assumption is that due to the rigidity of the moving objects, the frequency content of the trajectories will be similar after filling in their missing entries. The proposed rank estimation approach can be used in different computer vision problems, where the rank of a missing data matrix needs to be estimated. Experimental results with synthetic and real data are provided in order to empirically show the good performance of the proposed approach.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0924-9907 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ JSL2011; Serial 1710  
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Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa edit   pdf
url  doi
openurl 
  Title Multiple target tracking for intelligent headlights control Type Journal Article
  Year 2012 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 13 Issue (up) 2 Pages 594-605  
  Keywords Intelligent Headlights  
  Abstract Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1524-9050 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g Serial 1877  
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Author Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras edit  doi
openurl 
  Title Evaluation of Three Vision Based Object Perception Methods for a Mobile Robot Type Journal Article
  Year 2012 Publication Journal of Intelligent and Robotic Systems Abbreviated Journal JIRC  
  Volume 68 Issue (up) 2 Pages 185-208  
  Keywords  
  Abstract This paper addresses visual object perception applied to mobile robotics. Being able to perceive household objects in unstructured environments is a key capability in order to make robots suitable to perform complex tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Netherlands Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0921-0296 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RAV2012 Serial 2150  
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Author P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris Vintimilla; Angel Sappa edit  doi
openurl 
  Title Feature Point Descriptors: Infrared and Visible Spectra Type Journal Article
  Year 2014 Publication Sensors Abbreviated Journal SENS  
  Volume 14 Issue (up) 2 Pages 3690-3701  
  Keywords  
  Abstract This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.  
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  Notes ADAS;600.055; 600.076 Approved no  
  Call Number Admin @ si @ RCA2014a Serial 2474  
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Author Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados edit  doi
openurl 
  Title Efficient segmentation-free keyword spotting in historical document collections Type Journal Article
  Year 2015 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 48 Issue (up) 2 Pages 545–555  
  Keywords Historical documents; Keyword spotting; Segmentation-free; Dense SIFT features; Latent semantic analysis; Product quantization  
  Abstract In this paper we present an efficient segmentation-free word spotting method, applied in the context of historical document collections, that follows the query-by-example paradigm. We use a patch-based framework where local patches are described by a bag-of-visual-words model powered by SIFT descriptors. By projecting the patch descriptors to a topic space with the latent semantic analysis technique and compressing the descriptors with the product quantization method, we are able to efficiently index the document information both in terms of memory and time. The proposed method is evaluated using four different collections of historical documents achieving good performances on both handwritten and typewritten scenarios. The yielded performances outperform the recent state-of-the-art keyword spotting approaches.  
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  Notes DAG; ADAS; 600.076; 600.077; 600.061; 601.223; 602.006; 600.055 Approved no  
  Call Number Admin @ si @ RAT2015a Serial 2544  
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Author M. Olivera; Angel Sappa; Victor Santos edit  doi
openurl 
  Title A probabilistic approach for color correction in image mosaicking applications Type Journal Article
  Year 2015 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 14 Issue (up) 2 Pages 508 - 523  
  Keywords Color correction; image mosaicking; color transfer; color palette mapping functions  
  Abstract Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1057-7149 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ OSS2015b Serial 2554  
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Author Enric Marti; J.Roncaries; Debora Gil; Aura Hernandez-Sabate; Antoni Gurgui; Ferran Poveda edit  doi
openurl 
  Title PBL On Line: A proposal for the organization, part-time monitoring and assessment of PBL group activities Type Journal
  Year 2015 Publication Journal of Technology and Science Education Abbreviated Journal JOTSE  
  Volume 5 Issue (up) 2 Pages 87-96  
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  Notes IAM; ADAS; 600.076; 600.075 Approved no  
  Call Number Admin @ si @ MRG2015 Serial 2608  
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