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  • XIM-engine: a software framework to support the development of interactive applications that uses conscious and unconscious reactions in immersive mixed reality

     Omedas, Pedro; Betella, Albert; Zucca, Ricardo; Arsiwalla, Xerxes D.; Pacheco, Daniel; Wagner, Johannes; Lingenfelser, Florian; Andre, Elisabeth; Mazzei, Daniele; Lanatà, Antonio; Tognetti, Alessandro; Rossi, Danilo; Grau Saldes, Antoni; Goldhoorn, Alex; Guerra Paradas, Edmundo; Alquezar Mancho, Renato; Sanfeliu Cortes, Alberto; Verschure, Paul
    Virtual Reality International Conference
    p. Article No. 26-
    DOI: 10.1145/2617841.2620714
    Presentation's date: 2014-04-09
    Presentation of work at congresses

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    The development of systems that allow multimodal interpretation of human-machine interaction is crucial to advance our understanding and validation of theoretical models of user behavior. In particular, a system capable of collecting, perceiving and interpreting unconscious behavior can provide rich contextual information for an interactive system. One possible application for such a system is in the exploration of complex data through immersion, where massive amounts of data are generated every day both by humans and computer processes that digitize information at different scales and resolutions thus exceeding our processing capacity. We need tools that accelerate our understanding and generation of hypotheses over the datasets, guide our searches and prevent data overload. We describe XIM-engine, a bio-inspired software framework designed to capture and analyze multi-modal human behavior in an immersive environment. The framework allows performing studies that can advance our understanding on the use of conscious and unconscious reactions in interactive systems.

  • Visió artificial i sistemes intel.ligents

     Grau Saldes, Antoni; Alquezar Mancho, Renato; Climent Vilaro, Juan; Bolea Monte, Yolanda; Garrell Zulueta, Anais; Gamiz Caro, Juan; Manzanares Brotons, Manuel; Ferrer Minguez, Gonzalo; Amor Martinez, Adrian; Santamaria Navarro, Angel; Goldhoorn, Alex; Retamino Carrión, Eloy; Guerra Paradas, Edmundo; Trulls Fortuny, Eduard; Gamiz Caro, Javier Francisco; Sanfeliu Cortes, Alberto
    Competitive project

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  • Interacción, aprendizaje y cooperación robot-humano en areas urbanas

     Alquezar Mancho, Renato; Grau Saldes, Antoni; Serratosa Casanelles, Francesc; Climent Vilaro, Juan; Bolea Monte, Yolanda; Sanfeliu Cortes, Alberto
    Competitive project

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    Analysis of methods for playing human robot hide-and-seek in a simple real world urban environment  Open access

     Goldhoorn, Alex; Sanfeliu Cortes, Alberto; Alquezar Mancho, Renato
    Iberian Robotics Conference
    p. 505-520
    DOI: 10.1007/978-3-319-03653-3_37
    Presentation's date: 2013-11-28
    Presentation of work at congresses

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    The hide-and-seek game has many interesting aspects for studying cognitive functions in robots and the interactions between mobile robots and humans. Some MOMDP (Mixed Observable Markovian Decision Processes) models and a heuristic-based method are proposed and evaluated as an automated seeker. MOMDPs are used because the hider's position is not always known (partially observable), and the seeker's position is fully observable. The MOMDP model is used in an o-line method for which two reward functions are tried. Because the time complexity of this model grows exponentially with the number of (partially observable) states, an on-line hierarchical MOMDP model was proposed to handle bigger maps. To reduce the states in the on-line method a robot centered segmentation is used. In addition to extensive simulations, games with a human hider and a real mobile robot as a seeker have been done in a simple urban environment.

    Postprint (author’s final draft)

  • Comparison of MOMDP and heuristic methods to play hide-and-seek

     Goldhoorn, Alex; Sanfeliu Cortes, Alberto; Alquezar Mancho, Renato
    International Conference of the Catalan Association for Artificial Intelligence
    p. 31-40
    DOI: 10.3233/978-1-61499-320-9-31
    Presentation's date: 2013-10-23
    Presentation of work at congresses

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    The hide-and-seek game is considered an excellent domain for studying the interactions between mobile robots and humans. Prior to the implementation and test in our mobile robots TIBI and DABO, we have been devising different models and strategies to play this game and comparing them extensively in simulations. Recently, we have proposed the use of MOMDP (Mixed Observability Markov Decision Processes) models to learn a good policy to be applied by the seeker. Even though MOMDPs reduce the computational cost of POMDPs (Partially Observable MDPs), they still have a high computational complexity which is exponential with the number of states. For the hide-and-seek game, the number of states is directly related to the number of grid cells, and for two players (the hider and the seeker), it is the square of the number of cells. As an alternative to off-line MOMDP policy computation with the complete grid fine resolution, we have devised a two-level MOMDP, where the policy is computed on-line at the top level with a reduced number of states independent of the grid size. In this paper, we introduce a new fast heuristic method for the seeker and compare its performance to both off-line and on-line MOMDP approaches. We show simulation results in maps of different sizes against two types of automated hiders.

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    SVM-based classification of class C GPCRs from alignment-free physicochemical transformations of their sequences  Open access

     König, Caroline; Cruz Barbosa, Raúl; Alquezar Mancho, Renato; Vellido Alcacena, Alfredo
    International Conference on Image Analysis and Processing
    p. 336-343
    DOI: 10.1007/978-3-642-41190-8_36
    Presentation's date: 2013-09-09
    Presentation of work at congresses

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    G protein-coupled receptors (GPCRs) have a key function in regulating the function of cells due to their ability to transmit extracelullar signals. Given that the 3D structure and the functionality of most GPCRs is unknown, there is a need to construct robust classification models based on the analysis of their amino acid sequences for protein homology detection. In this paper, we describe the supervised classification of the different subtypes of class C GPCRs using support vector machines (SVMs). These models are built on different transformations of the amino acid sequences based on their physicochemical properties. Previous research using semi-supervised methods on the same data has shown the usefulness of such transformations. The obtained classification models show a robust performance, as their Matthews correlation coefficient is close to 0.91 and their prediction accuracy is close to 0.93. © 2013 Springer-Verlag.

    G protein-coupled receptors (GPCRs) have a key function in regulating the function of cells due to their ability to transmit extracelullar signals. Given that the 3D structure and the functionality of most GPCRs is unknown, there is a need to construct robust classification models based on the analysis of their amino acid sequences for protein homology detection. In this paper, we describe the supervised classification of the different subtypes of class C GPCRs using support vector machines (SVMs). These models are built on different transformations of the amino acid sequences based on their physicochemical properties. Previous research using semi-supervised methods on the same data has shown the usefulness of such transformations. The obtained classification models show a robust performance, as their Matthews correlation coefficient is close to 0.91 and their prediction accuracy is close to 0.93.

    Postprint (author’s final draft)

  • Smooth point-set registration using neighboring constraints

     Sanromà Güell, Gerard; Alquezar Mancho, Renato; Serratosa Casanelles, Francesc; Herrera, Blas
    Pattern recognition letters
    Vol. 33, num. 15, p. 2029-2037
    DOI: 10.1016/j.patrec.2012.04.008
    Date of publication: 2012-11-01
    Journal article

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  • A probabilistic integrated object recognition and tracking framework

     Serratosa Casanelles, Francesc; Alquezar Mancho, Renato; Amézquita Gómez, Nicolás
    Expert systems with applications
    Vol. 39, num. 8, p. 7302-7318
    DOI: 10.1016/j.eswa.2012.01.088
    Date of publication: 2012-06-15
    Journal article

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  • Quantitative and qualitative approaches for stock movement prediction

     Petchamé, Jordi; Nebot Castells, Maria Angela; Alquezar Mancho, Renato
    DOI: 10.3233/978-1-61499-139-7-233
    Date of publication: 2012
    Book chapter

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  • Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks

     Romero Merino, Enrique; Alquezar Mancho, Renato
    Neural networks
    Vol. 25, num. 1, p. 122-129
    DOI: 10.1016/j.neunet.2011.08.005
    Date of publication: 2012-01
    Journal article

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    Recently, error minimized extreme learning machines (EM-ELMs) have been proposed as a simple and efficient approach to build single-hidden-layer feed-forward networks (SLFNs) sequentially. They add random hidden nodes one by one (or group by group) and update the output weights incrementally to minimize the sum-of-squares error in the training set. Other very similar methods that also construct SLFNs sequentially had been reported earlier with the main difference that their hidden-layer weights are a subset of the data instead of being random. These approaches are referred to as support vector sequential feed-forward neural networks (SV-SFNNs), and they are a particular case of the sequential approximation with optimal coefficients and interacting frequencies (SAOCIF) method. In this paper, it is firstly shown that EM-ELMs can also be cast as a particular case of SAOCIF. In particular, EM-ELMs can easily be extended to test some number of random candidates at each step and select the best of them, as SAOCIF does. Moreover, it is demonstrated that the cost of the computation of the optimal outputlayer weights in the originally proposed EM-ELMs can be improved if it is replaced by the one included in SAOCIF. Secondly, we present the results of an experimental study on 10 benchmark classification and 10 benchmark regression data sets, comparing EM-ELMs and SV-SFNNs, that was carried out under the same conditions for the two models. Although both models have the same (efficient) computational cost, a statistically significant improvement in generalization performance of SV-SFNNs vs. EM-ELMs was found in 12 out of the 20 benchmark problems.

  • A new graph matching method for point-set correspondence using the EM algorithm and Softassign

     Sanromà Güell, Gerard; Alquezar Mancho, Renato; Serratosa Casanelles, Francesc
    Computer vision and image understanding
    Vol. 116, num. 2, p. 292-304
    DOI: 10.1016/j.cviu.2011.10.009
    Date of publication: 2012
    Journal article

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    Finding correspondences between two point-sets is a common step in many vision applications (e.g., image matching or shape retrieval). We present a graph matching method to solve the point-set correspondence problem, which is posed as one of mixture modelling. Our mixture model encompasses a model of structural coherence and a model of affine-invariant geometrical errors. Instead of absolute positions, the geometrical positions are represented as relative positions of the points with respect to each other. We derive the Expectation–Maximization algorithm for our mixture model. In this way, the graph matching problem is approximated, in a principled way, as a succession of assignment problems which are solved using Softassign. Unlike other approaches, we use a true continuous underlying correspondence variable. We develop effective mechanisms to detect outliers. This is a useful technique for improving results in the presence of clutter. We evaluate the ability of our method to locate proper matches as well as to recognize object categories in a series of registration and recognition experiments. Our method compares favourably to other graph matching methods as well as to point-set registration methods and outlier rejectors.

  • Quantitative and qualitative approaches for stock movement prediction

     Petchamé, Jordi; Nebot Castells, Maria Angela; Alquezar Mancho, Renato
    International Conference of the Catalan Association for Artificial Intelligence
    p. 233-242
    DOI: 10.3233/978-1-61499-139-7-233
    Presentation's date: 2012
    Presentation of work at congresses

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  • Group-wise sparse correspondences between images based on a common labelling approach

     Solé Ribalta, Albert; Sanromà, Gerard; Serratosa Casanelles, Francesc; Alquezar Mancho, Renato
    International Conference on Computer Vision Theory and Applications
    p. 269-278
    Presentation's date: 2012
    Presentation of work at congresses

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    Finding sparse correspondences between two images is a usual process needed for several higher-level computer vision tasks. For instance, in robot positioning, it is frequent to make use of images that the robot captures from their cameras to guide the localisation or reduce the intrinsic ambiguity of a specific localisation obtained by other methods. Nevertheless, obtaining good correspondence between two images with a high degree of dissimilarity is a complex task that may lead to important positioning errors. With the aim of increasing the accuracy with respect to the pair-wise image matching approaches, we present a new method to compute group-wise correspondences among a set of images. Thus, pair-wise errors are compensated and better correspondences between images are obtained. These correspondences can be used as a less-noisy input for the localisation process. Group-wise correspondences are computed by finding the common labelling of a set of salient points obtained from the images. Results show a clear increase in effectiveness with respect to methods that use only two images.

  • AERIAL ROBOTICS COOPERATIVE ASSEMBLY SYSTEM

     Andrade Cetto, Juan; Grau Saldes, Antoni; Alquezar Mancho, Renato; Moreno Noguer, Francesc d'Assis; Villamizar Vergel, Michael Alejandro; Bolea Monte, Yolanda; Sanfeliu Cortes, Alberto
    Competitive project

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    Smooth simultaneous structural graph matching and point-set registration  Open access

     Sanromà Güell, Gerard; Alquezar Mancho, Renato; Serratosa Casanelles, Francesc
    TC-15 Workshop on Graph-Based Representations in Pattern Recognition
    p. 142-151
    DOI: 10.1007/978-3-642-20844-7_15
    Presentation's date: 2011
    Presentation of work at congresses

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    We present a graph matching method that encompasses both a model of structural consistency and a model of geometrical deformations. Our method poses the graph matching problem as one of mixture modelling which is solved using the EM algorithm. The solution is then approximated as a succession of assignment problems which are solved, in a smooth way, using Softassign. Our method allows us to detect outliers in both graphs involved in the matching. Unlike the outlier rejectors such as RANSAC and Graph Transformation Matching, our method is able to refine an initial the tentative correspondence-set in a more flexible way than simply removing spurious correspondences. In the experiments, our method shows a good ratio between effectiveness and computational time compared with other methods inside and outside the graphs’ field.

    Postprint (author’s final draft)

  • THE COLLECTIVE EXPERIENCE OF EMPATHIC DATA SYSTEMS

     Bolea Monte, Yolanda; Grau Saldes, Antoni; Alquezar Mancho, Renato; Andrade Cetto, Juan; Corominas Murtra, Andreu; Sanfeliu Cortes, Alberto
    Competitive project

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    A discrete labelling approach to attributed graph matching using SIFT features  Open access

     Sanromà Güell, Gerard; Alquezar Mancho, Renato; Serratosa Casanelles, Francesc
    International Conference on Pattern Recognition
    p. 954-957
    DOI: 10.1109/ICPR.2010.239
    Presentation's date: 2010
    Presentation of work at congresses

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    Local invariant feature extraction methods are widely used for image-features matching. There exist a number of approaches aimed at the refinement of the matches between image-features. It is a common strategy among these approaches to use geometrical criteria to reject a subset of outliers. One limitation of the outlier rejection design is that it is unable to add new useful matches. We present a new model that integrates the local information of the SIFT descriptors along with global geometrical information to estimate a new robust set of feature-matches. Our approach encodes the geometrical information by means of graph structures while posing the estimation of the feature-matches as a graph matching problem. Some comparative experimental results are presented.

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    Graph matching using SIFT descriptors: an application to pose recovery of a mobile robot  Open access

     Sanromà Güell, Gerard; Alquezar Mancho, Renato; Serratosa Casanelles, Francesc
    International Conference on Computer Vision Theory and Applications
    p. 249-254
    Presentation's date: 2010
    Presentation of work at congresses

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    Image-feature matching based on Local Invariant Feature Extraction (LIFE) methods has proven to be successful, and SIFT is one of the most effective. SIFT matching uses only local texture information to compute the correspondences. A number of approaches have been presented aimed at enhancing the image-features matches computed using only local information such as SIFT. What most of these approaches have in common is that they use a higher level information such as spatial arrangement of the feature points to reject a subset of outliers. The main limitation of the outlier rejectors is that they are not able to enhance the configuration of matches by adding new useful ones. In the present work we propose a graph matching algorithm aimed not only at rejecting erroneous matches but also at selecting additional useful ones. We use both the graph structure to encode the geometrical information and the SIFT descriptors in the node’s attributes to provide local texture information. This algorithm is an ensemble of successful ideas previously reported by other researchers. We demonstrate the effectiveness of our algorithm in a pose recovery application.

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    Attributed graph matching for image-features association using SIFT descriptors  Open access

     Sanromà Güell, Gerard; Alquezar Mancho, Renato; Serratosa Casanelles, Francesc
    International Workshop on Structural and Syntactic Pattern Recognition
    p. 254-263
    DOI: 10.1007/978-3-642-14980-1_24
    Presentation's date: 2010
    Presentation of work at congresses

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    Image-features matching based on SIFT descriptors is subject to the misplacement of certain matches due to the local nature of the SIFT representations. Some well-known outlier rejectors aim to remove those misplaced matches by imposing geometrical consistency. We present two graph matching approaches (one continuous and one discrete) aimed at the matching of SIFT features in a geometrically consistent way. The two main novelties are that, both local and contextual coherence are imposed during the optimization process and, a model of structural consistency is presented that accounts for the quality rather than the quantity of the surrounding matches. Experimental results show that our methods achieve good results under various types of noise.

    Postprint (author’s final draft)

  • Object recognition and tracking in video sequences: a new integrated methodology

     Amézquita Gómez, Nicolás; Alquezar Mancho, Renato; Serratosa Casanelles, Francesc
    11th Iberoamerican Congress in Pattern Recognition
    p. 481-490
    DOI: 10.1007/11892755_50
    Presentation's date: 2009-11-14
    Presentation of work at congresses

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    Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks  Open access

     Arizmendi Pereira, Carlos Julio; Romero Merino, Enrique; Alquezar Mancho, Renato; Caminal Magrans, Pedro; Díaz, Ivan; Benito Vales, Salvador; Giraldo Giraldo, Beatriz F.
    IEEE Engineering in Medicine and Biology Society
    p. 4343-4346
    DOI: 10.1109/IEMBS.2009.5332742
    Presentation's date: 2009-09
    Presentation of work at congresses

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    The process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 23 patients with successful test but that had to be reintubated before 48 hours (group R). Each patient was characterized using 8 time series and 6 statistics extracted from respiratory and cardiac signals. A moving window statistical analysis was applied obtaining for each patient a sequence of patterns of 48 features. Applying a cluster analysis two groups with the majority dataset were obtained. Neural networks were applied to discriminate between patients from groups S, F and R. The best performance obtained was 84.0% of well classified patients using a linear perceptron trained with a feature selection procedure (that selected 19 of the 48 features) and taking as input the main cluster centroid. However, the classification baseline 69.8% could not be improved when using the original set of patterns instead of the centroids to classify the patients.

  • Learning policies for blocks

     González Romero, A; González Camargo, A; Alquezar Mancho, Renato
    IASTED International Conference on Artificial Intelligence and Applications, AIA 2009
    p. 135-139
    Presentation's date: 2009-02-17
    Presentation of work at congresses

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  • Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks

     Arizmendi Pereira, Carlos Julio; Romero Merino, Enrique; Alquezar Mancho, Renato; Caminal Magrans, Pedro; Diaz, I; Salvador Vales, Benito; Giraldo Giraldo, Beatriz F.
    Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    Vol. 1, p. 4343-4346
    Date of publication: 2009
    Journal article

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    Tracking deformable objects and dealing with same class object occlusion  Open access

     Alquezar Mancho, Renato; Amézquita, N; Serratosa Casanelles, Francesc
    Date of publication: 2009
    Book chapter

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    This paper presents an extension of a previously reported method for object tracking in video sequences to handle the problems of object crossing and occlusion by other objects in the same class that the one followed. The proposed solution is embedded in a system that integrates recognition and tracking in a probabilistic framework. In a recent work, a method to approach the object occlusion problem was proposed that failed when the object crossed or was occluded by another object of the same class. Here we present an attempt to overcome this limitation and show some promising results. The method is based on the assumption that when two objects cross each other there is not a brusque change of the trajectories. Our system uses object recognition results provided by a neural net that are computed from colour features of image regions for each frame. The location of tracked objects is represented through probability images that are updated dynamically using both recognition and tracking results. From these probabilities and a prediction of the motion of the object in the image, a binary decision is made for each pixel and object.

    Postprint (author’s final draft)

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    Experimental assessment of probabilistic integrated object recognition and tracking methods  Open access

     Serratosa Casanelles, Francesc; Amézquita Gómez, Nicolás; Alquezar Mancho, Renato
    Iberoamerican Congress on Pattern Recognition
    p. 817-824
    DOI: 10.1007/978-3-642-10268-4_96
    Presentation's date: 2009
    Presentation of work at congresses

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    This paper presents a comparison of two classifiers that are used as a first step within a probabilistic object recognition and tracking framework called PIORT. This first step is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. One of the implemented classifiers is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results show that, on one hand, both classifiers (although they are very different approaches) yield a similar performance when they are integrated within the tracking framework. And on the other hand, our object recognition and tracking framework obtains good results when compared to other published tracking methods in video sequences taken with a moving camera and including total and partial occlusions of the tracked object.

  • Building Policies for Scrabble

     Alquezar Mancho, Renato
    Onzè Congrés Internacional de l'Associació Catalana d'Intelligència Artificial
    Presentation's date: 2008-10-24
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  • Building Policies for scrabble

     Alejandro, González Romero; Alquezar Mancho, Renato
    Onzè Congrés Internacional de l'Associació Catalana d'Intelligència Artificial
    p. 342-351
    DOI: 10/3233/978-1-58603-925-7-342
    Presentation's date: 2008-10-22
    Presentation of work at congresses

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  • Patients on weaning trials classified with neural networks and feature selection

     Giraldo Giraldo, Beatriz F.; Arizmendi Pereira, Carlos Julio; Romero Merino, Enrique; Alquezar Mancho, Renato; Caminal Magrans, Pedro; Salvador Vales, Benito
    Date of publication: 2008-09
    Book chapter

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  • A heuristic for Scrabble based in probability

     González Romero, Alejandro; González Acuña, Francisco; Ramirez Florez, Arturo; Hernández, Enric; Roldán Aguilar, Amador; Alquezar Mancho, Renato
    18th European Conference on Artificial Intelligence
    p. 35-39
    Presentation's date: 2008-07-21
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  • Dealing with occlusion in a probabilistic object tracking method

     Amézquita Gómez, Nicolás; Alquezar Mancho, Renato; Serratosa Casanelles, Francesc
    IEEE Conference on Computer Vision and Pattern Recognition
    Presentation's date: 2008-06-28
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  • Building policies for scrabble

     González, Alejandro; Alquezar Mancho, Renato
    Frontiers in artificial intelligence and applications
    Vol. 184, p. 342-351
    DOI: 10.3233/978-1-58603-925-7-342
    Date of publication: 2008
    Journal article

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  • Improving the matching of graphs generated from shapes by the use of Procrustes distances into a clique-based MAP formulation

     Sanromà Güell, Gerard; Serratosa Casanelles, Francesc; Alquezar Mancho, Renato
    International Conference on Pattern Recognition
    p. 1-4
    Presentation's date: 2008
    Presentation of work at congresses

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  • Shape learning with function-described graphs

     Sanromà, Gerard; Serratosa Casanelles, Francesc; Alquezar Mancho, Renato
    Lecture notes in computer science
    Vol. 5112, p. 475-484
    DOI: 10.1007/978-3-540-69812-8_47
    Date of publication: 2008
    Journal article

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  • Hybrid genetic algorithm and procrustes analysis for enhancing the matching of graphs generated from shapes

     Sanromà, Gerard; Serratosa Casanelles, Francesc; Alquezar Mancho, Renato
    Lecture notes in computer science
    Vol. 5342, p. 298-307
    DOI: 10.1007/978-3-540-89689-0_34
    Date of publication: 2008
    Journal article

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  • Heuristics for the selection of weights in sequential feed-forward neural networks: An experimental study

     Romero Merino, Enrique; Alquezar Mancho, Renato
    Neurocomputing
    Vol. 70, num. 16-18, p. 2735-2743
    DOI: 10.1016/j.neucom.2006.05.022
    Date of publication: 2007-10
    Journal article

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  • Robotica Ubicua para entornos urbanos. (DPI2007-61452)

     Alquezar Mancho, Renato; Sanfeliu Cortes, Alberto
    Competitive project

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  • Parallelization of bioinspired convolutional networks for object recognition using the gpu

     Serra Ruiz, Oscar; Sanfeliu Cortes, Alberto; Alquezar Mancho, Renato
    Date: 2007-07
    Report

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  • A new method for object tracking based on regions instead of contours

     Amézquita Gómez, Nicolás; Alquezar Mancho, Renato; Serratosa Casanelles, Francesc
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 1-8
    Presentation's date: 2007-06-22
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  • CONSOLIDER-INGENIO 2010 Multimodal interaction in pattern recognition and computer vision

     Sanfeliu Cortes, Alberto; Alquezar Mancho, Renato; Moreno Noguer, Francesc d'Assis; Ferrer Sumsi, Miquel
    Competitive project

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  • Combining neural networks and clustering techniques for object recognition in indoor video sequences

     Alquezar Mancho, Renato
    11th Iberoamerican Congress in Pattern Recognition
    Presentation's date: 2006-11-15
    Presentation of work at congresses

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  • Object recognition and tracking in video sequences: a new integrated methodology

     Alquezar Mancho, Renato
    11th Iberoamerican Congress in Pattern Recognition
    Presentation's date: 2006-11-15
    Presentation of work at congresses

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  • Combining neural networks and clustering techniques for object recognition in indoor video sequences

     Serratosa Casanelles, Francesc; Amézquita Gómez, Nicolás; Alquezar Mancho, Renato
    11th Iberoamerican Congress in Pattern Recognition
    p. 399-405
    DOI: 10.1007/11892755_41
    Presentation's date: 2006-11-14
    Presentation of work at congresses

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  • Clasificación de pacientes en proceso de extubacion mediante redes neuronales y selección características

     Giraldo Giraldo, Beatriz F.; Arizmendi Pereira, Carlos Julio; Romero Merino, Enrique; Alquezar Mancho, Renato; Caminal Magrans, Pedro; Salvador Vales, Benito; Ballesteros Carrillo, David
    Congreso Anual de la Sociedad Española de Ingeniería Biomédica
    p. 281-284
    Presentation's date: 2006-11-06
    Presentation of work at congresses

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  • Combining neural networks and clustering techniques for object recognition in indoor video sequences

     Serratosa Casanelles, Francesc; Amézquita Gómez, Nicolás; Alquezar Mancho, Renato
    Lecture notes in computer science
    Vol. 4225, p. 399-405
    DOI: 10.1007/11892755_41
    Date of publication: 2006-10-14
    Journal article

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  • Diseño de un sistema de apoyo a la decisión en oncología clínica basado en métodos avanzados de soft computing. TIN2006-08114

     Vellido Alcacena, Alfredo; Alquezar Mancho, Renato; Escobet Canal, Antoni; Belanche Muñoz, Luis Antonio
    Competitive project

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  • Object recognition and tracking in video sequences: a new integrated methodology

     Amézquita Gómez, Nicolás; Alquezar Mancho, Renato; Serratosa Casanelles, Francesc
    Lecture notes in computer science
    Vol. 4225, p. 481-490
    DOI: 10.1007/11892755_50
    Date of publication: 2006-09
    Journal article

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  • Patients on weaning trials from mechanical ventilation classified with neural networks and feature selection

     Giraldo Giraldo, Beatriz F.; Arizmendi Pereira, Carlos Julio; Romero Merino, Enrique; Alquezar Mancho, Renato; Caminal Magrans, Pedro; Benito Vales, Salvador; Ballesteros Carrillo, David
    IEEE Engineering in Medicine and Biology Society
    p. 2195-2198
    Presentation's date: 2006-08-30
    Presentation of work at congresses

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  • A sequential algorithm for feed-forward neural networks with optimal coefficients and interacting frequencies

     Romero Merino, Enrique; Alquezar Mancho, Renato
    Neurocomputing
    Vol. 69, num. 13-15, p. 1540-1552
    DOI: 10.1016/j.neucom.2005.07.006
    Date of publication: 2006-08
    Journal article

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