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Eloi Puertas, Sergio Escalera, & Oriol Pujol. (2011). Multi-Class Multi-Scale Stacked Sequential Learning. In Carlo Sansone, Josef Kittler, & Fabio Roli (Eds.), 10th International Conference on Multiple Classifier Systems (Vol. 6713, pp. 197–206). Springer.
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Miguel Angel Bautista, Oriol Pujol, Xavier Baro, & Sergio Escalera. (2011). Introducing the Separability Matrix for Error Correcting Output Codes Coding. In Carlo Sansone, Josef Kittler, & Fabio Roli (Eds.), 10th International Conference on Multiple Classifier Systems (Vol. 6713, pp. 227–236). LNCS. Springer-Verlag Berlin, Heidelberg.
Abstract: Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results.
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Firat Ismailoglu, Ida G. Sprinkhuizen-Kuyper, Evgueni Smirnov, Sergio Escalera, & Ralf Peeters. (2015). Fractional Programming Weighted Decoding for Error-Correcting Output Codes. In Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 (pp. 38–50). Springer International Publishing.
Abstract: In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments.
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Eugenio Alcala, Laura Sellart, Vicenc Puig, Joseba Quevedo, Jordi Saludes, David Vazquez, et al. (2016). Comparison of two non-linear model-based control strategies for autonomous vehicles. In 24th Mediterranean Conference on Control and Automation (pp. 846–851).
Abstract: This paper presents the comparison of two nonlinear model-based control strategies for autonomous cars. A control oriented model of vehicle based on a bicycle model is used. The two control strategies use a model reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a non-linear control law that is designed by means of the Lyapunov direct approach. The second approach is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high order sliding mode control. To test and compare the proposed control strategies, different path following scenarios are used in simulation.
Keywords: Autonomous Driving; Control
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Aniol Lidon, Xavier Giro, Marc Bolaños, Petia Radeva, Markus Seidl, & Matthias Zeppelzauer. (2015). UPC-UB-STP @ MediaEval 2015 diversity task: iterative reranking of relevant images. In 2015 MediaEval Retrieving Diverse Images Task.
Abstract: This paper presents the results of the UPC-UB-STP team in the 2015 MediaEval Retrieving Diverse Images Task. The goal of the challenge is to provide a ranked list of Flickr photos for a predefined set of queries. Our approach firstly generates a ranking of images based on a query-independent estimation of its relevance. Only top results are kept and iteratively re-ranked based on their intra-similarity to introduce diversity.
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Laura Lopez-Fuentes, Joost Van de Weijer, Marc Bolaños, & Harald Skinnemoen. (2017). Multi-modal Deep Learning Approach for Flood Detection. In MediaEval Benchmarking Initiative for Multimedia Evaluation.
Abstract: In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a Convolutional Neural Network which extracts the visual features and a bidirectional Long Short-Term Memory network to extract the semantic features from the textual metadata. We validate the
method on images extracted from Flickr which contain both visual information and metadata and compare the results when using both, visual information only or metadata only. This work has been done in the context of the MediaEval Multimedia Satellite Task.
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Laura Lopez-Fuentes, Alessandro Farasin, Harald Skinnemoen, & Paolo Garza. (2018). Deep Learning models for passability detection of flooded roads. In MediaEval 2018 Multimedia Benchmark Workshop (Vol. 2283).
Abstract: In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task.
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Mohamed Ali Souibgui, Y.Kessentini, & Alicia Fornes. (2020). A conditional GAN based approach for distorted camera captured documents recovery. In 4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence.
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Mireia Sole, Joan Blanco, Debora Gil, G. Fonseka, Richard Frodsham, Oliver Valero, et al. (2017). Is there a pattern of Chromosome territoriality along mice spermatogenesis? In 3rd Spanish MeioNet Meeting Abstract Book (pp. 55–56).
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Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2011). Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach.ased Approach. In K. Djemal (Ed.), 2nd International Workshop on Medical Image Analysis and Descriptionfor Diagnosis Systems (pp. 62–71). SciTePress.
Abstract: In this paper we present our approach on real-time polyp detection in colonoscopy videos. Our method consists of three stages: Image Segmentation, Region Description and Image Classification. Taking into account the constraints of our project, we introduce our segmentation system that is based on the model of appearance of the polyp that we have defined after observing real videos from colonoscopy processes. The output of this stage will ideally be a low number of regions of which one of them should cover the whole polyp region (if there is one in the image). This regions will be described in terms of features and, as a result of a machine learning schema, classified based on the values that they have for the several features that we will use on their description. Although we are still on the early stages of the project, we present some preliminary segmentation results that indicates that we are going in a good direction.
Keywords: Medical Imaging, Colonoscopy, Pattern Recognition, Segmentation, Polyp Detection, Region Description, Machine Learning, Real-time.
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Carlo Gatta, Oriol Pujol, Oriol Rodriguez-Leor, J. Mauri, & Petia Radeva. (2008). Robust Image-based IVUS Pullbacks Gating. In Proceedings 11th International ConferenceMedical Image Computing and Computer–Assisted Intervention (Vol. 5242, 518–525). LNCS.
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Carlo Gatta, Oriol Pujol, Oriol Rodriguez-Leor, J. Mauri, & Petia Radeva. (2008). Improved Rigid Registration of Vessel Structures using the Fast Radial Symmetry Transform. In Computer Vision for Intravascular Imaging CVII’08 Workshop Medical Image Computing and Computer–Assisted Intervention , 11th International Conference (128–136).
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Francesco Ciompi, Oriol Pujol, E Fernandez-Nofrerias, J. Mauri, & Petia Radeva. (2009). ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences. In 12th International Conference on Medical Image and Computer Assisted Intervention (Vol. 5762). LNCS. Springer Berlin Heidelberg.
Abstract: The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers.
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Carlo Gatta, Simone Balocco, Francesco Ciompi, R. Hemetsberger, Oriol Rodriguez-Leor, & Petia Radeva. (2010). Real-time gating of IVUS sequences based on motion blur analysis: Method and quantitative validation. In 13th international conference on Medical image computing and computer-assisted intervention (Vol. II, pp. 59–67). Springer-Verlag Berlin.
Abstract: Intravascular Ultrasound (IVUS) is an image-guiding technique for cardiovascular diagnostic, providing cross-sectional images of vessels. During the acquisition, the catheter is pulled back (pullback) at a constant speed in order to acquire spatially subsequent images of the artery. However, during this procedure, the heart twist produces a swinging fluctuation of the probe position along the vessel axis. In this paper we propose a real-time gating algorithm based on the analysis of motion blur variations during the IVUS sequence. Quantitative tests performed on an in-vitro ground truth data base shown that our method is superior to state of the art algorithms both in computational speed and accuracy.
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Francesco Ciompi, Oriol Pujol, Carlo Gatta, Xavier Carrillo, J. Mauri, & Petia Radeva. (2011). A Holistic Approach for the Detection of Media-Adventitia Border in IVUS. In 14th International Conference on Medical Image Computing and Computer Assisted Intervention (Vol. 6893, pp. 401–408). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we present a methodology for the automatic detection of media-adventitia border (MAb) in Intravascular Ultrasound. A robust computation of the MAb is achieved through a holistic approach where the position of the MAb with respect to other tissues of the vessel is used. A learned quality measure assures that the resulting MAb is optimal with respect to all other tissues. The mean distance error computed through a set of 140 images is 0.2164 (±0.1326) mm.
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