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Oriol Pujol, & David Masip. (2009). Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6), 1140–1146.
Abstract: This article introduces a novel binary discriminative learning technique based on the approximation of the non-linear decision boundary by a piece-wise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points – points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and non-linear behavior is obtained. The simplicity of the method allows its extension to cope with some of nowadays machine learning challenges, such as online learning, large scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database. Finally, we apply our technique in online and large scale scenarios, and in six real life computer vision and pattern recognition problems: gender recognition, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease severity detection, clef classification and action recognition using a 3D accelerometer data. The results are promising and this paper opens a line of research that deserves further attention
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Xavier Baro. (2009). Probabilistic Darwin Machines: A New Approach to Develop Evolutionary Object Detection (Jordi Vitria, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Ever since computers were invented, we have wondered whether they might perform some of the human quotidian tasks. One of the most studied and still nowadays less understood problem is the capacity to learn from our experiences and how we generalize the knowledge that we acquire. One of that unaware tasks for the persons and that more interest is awakening in different scientific areas since the beginning, is the one that is known as pattern recognition. The creation of models that represent the world that surrounds us, help us for recognizing objects in our environment, to predict situations, to identify behaviors... All this information allows us to adapt ourselves and to interact with our environment. The capacity of adaptation of individuals to their environment has been related to the amount of patterns that are capable of identifying.
This thesis faces the pattern recognition problem from a Computer Vision point of view, taking one of the most paradigmatic and extended approaches to object detection as starting point. After studying this approach, two weak points are identified: The first makes reference to the description of the objects, and the second is a limitation of the learning algorithm, which hampers the utilization of best descriptors.
In order to address the learning limitations, we introduce evolutionary computation techniques to the classical object detection approach.
After testing the classical evolutionary approaches, such as genetic algorithms, we develop a new learning algorithm based on Probabilistic Darwin Machines, which better adapts to the learning problem. Once the learning limitation is avoided, we introduce a new feature set, which maintains the benefits of the classical feature set, adding the ability to describe non localities. This combination of evolutionary learning algorithm and features is tested on different public data sets, outperforming the results obtained by the classical approach.
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Laura Igual, & Xavier Baro. (2013). Experiencia de aprendizaje de programación basada en proyectos. Simposio-Taller Estrategias y herramientas para el aprendizaje y la evaluación.
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Xavier Baro, David Masip, Elena Planas, & Julia Minguillon. (2013). PeLP: Plataforma para el Aprendizaje de Lenguajes de Programación.
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Miquel Ferrer, Robert Benavente, Ernest Valveny, J. Garcia, Agata Lapedriza, & Gemma Sanchez. (2008). Aprendizaje Cooperativo Aplicado a la Docencia de las Asignaturas de Programacion en Ingenieria Informatica.
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Robert Benavente, Ernest Valveny, Jaume Garcia, Agata Lapedriza, Miquel Ferrer, & Gemma Sanchez. (2008). Una experiencia de adaptacion al EEES de las asignaturas de programacion en Ingenieria Informatica.
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Maria Vanrell, & Jordi Vitria. (1993). Mathematical Morphology, Granulometries and Texture Perception..
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X. Binefa, Jordi Vitria, & Maria Vanrell. (1992). Reconstruccion tridimensional de imagenes Microscopicas..
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Maria Vanrell, & Jordi Vitria. (1997). Optimal 3x3 decomposable disks for morphological transformations. Image and Vision Computing, 15(2): 845–854.
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Santiago Segui, Laura Igual, Fernando Vilariño, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, et al. (2008). Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy. In and J.K. Tsotsos M. V. A. Gasteratos (Ed.), Computer Vision Systems. 6th International (Vol. 5008, 251–260). LNCS. Berlin Heidelberg: Springer-Verlag.
Abstract: Wireless Video Capsule Endoscopy is a clinical technique consisting of the analysis of images from the intestine which are pro- vided by an ingestible device with a camera attached to it. In this paper we propose an automatic system to diagnose severe intestinal motility disfunctions using the video endoscopy data. The system is based on the application of computer vision techniques within a machine learn- ing framework in order to obtain the characterization of diverse motil- ity events from video sequences. We present experimental results that demonstrate the effectiveness of the proposed system and compare them with the ground-truth provided by the gastroenterologists.
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Bogdan Raducanu, & Jordi Vitria. (2007). Online Learning for Human-Robot Interaction. In IEEE Conference on Computer Vision and Pattern Recognition Workshop on.
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Elvina Motard, Bogdan Raducanu, Viviane Cadenat, & Jordi Vitria. (2007). Incremental On-Line Topological Map Learning for A Visual Homing Application. In IEEE International Conference on Robotics and Automation (2049–2054).
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Agata Lapedriza, David Masip, & Jordi Vitria. (2007). A Hierarchical Approach for Multi-task Logistic Regression. In J. Marti et al. (Ed.), 3rd Iberian Conference on Pattern Recognition and Image Analysis (Vol. 4478, 258–265). LNCS.
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David Masip, Agata Lapedriza, & Jordi Vitria. (2008). Multitask Learning: An Application to Incremental Face Recognition. In 3rd International Conference on Computer Vision Theory and Applications (Vol. 1, 585–590).
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Agata Lapedriza, David Masip, & Jordi Vitria. (2008). Subject Recognition Using a New Approach for Feature Extraction. In 3rd International Conference on Computer Vision Theory and Applications (Vol. 2, 61–66).
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