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Author |
Mariella Dimiccoli |
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Title |
Fundamentals of cone regression |
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2016 |
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Journal of Statistics Surveys |
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10 |
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53-99 |
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cone regression; linear complementarity problems; proximal operators. |
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Cone regression is a particular case of quadratic programming that minimizes a weighted sum of squared residuals under a set of linear inequality constraints. Several important statistical problems such as isotonic, concave regression or ANOVA under partial orderings, just to name a few, can be considered as particular instances of the cone regression problem. Given its relevance in Statistics, this paper aims to address the fundamentals of cone regression from a theoretical and practical point of view. Several formulations of the cone regression problem are considered and, focusing on the particular case of concave regression as an example, several algorithms are analyzed and compared both qualitatively and quantitatively through numerical simulations. Several improvements to enhance numerical stability and bound the computational cost are proposed. For each analyzed algorithm, the pseudo-code and its corresponding code in Matlab are provided. The results from this study demonstrate that the choice of the optimization approach strongly impacts the numerical performances. It is also shown that methods are not currently available to solve efficiently cone regression problems with large dimension (more than many thousands of points). We suggest further research to fill this gap by exploiting and adapting classical multi-scale strategy to compute an approximate solution. |
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1935-7516 |
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MILAB; |
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no |
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Admin @ si @Dim2016a |
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2783 |
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Author |
Mariano Vazquez; Ruth Aris; Guillaume Hozeaux; R.Aubry; P.Villar;Jaume Garcia ; Debora Gil; Francesc Carreras |
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Title |
A massively parallel computational electrophysiology model of the heart |
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Journal Article |
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Year |
2011 |
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International Journal for Numerical Methods in Biomedical Engineering |
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IJNMBE |
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27 |
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1911-1929 |
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computational electrophysiology; parallelization; finite element methods |
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This paper presents a patient-sensitive simulation strategy capable of using the most efficient way the high-performance computational resources. The proposed strategy directly involves three different players: Computational Mechanics Scientists (CMS), Image Processing Scientists and Cardiologists, each one mastering its own expertise area within the project. This paper describes the general integrative scheme but focusing on the CMS side presents a massively parallel implementation of computational electrophysiology applied to cardiac tissue simulation. The paper covers different angles of the computational problem: equations, numerical issues, the algorithm and parallel implementation. The proposed methodology is illustrated with numerical simulations testing all the different possibilities, ranging from small domains up to very large ones. A key issue is the almost ideal scalability not only for large and complex problems but also for medium-size meshes. The explicit formulation is particularly well suited for solving this highly transient problems, with very short time-scale. |
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Swansea (UK) |
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John Wiley & Sons, Ltd. |
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John Wiley & Sons, Ltd. |
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IAM |
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no |
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IAM @ iam @ VAH2011 |
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1198 |
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Maria Vanrell; Ramon Baldrich; Anna Salvatella; Robert Benavente; Francesc Tous |
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Title |
Induction operators for a computational colour-texture representation |
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2004 |
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Computer Vision and Image Understanding, 94(1–3):92–114, ISSN: 1077–3142 (IF: 0.651) |
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CIC |
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CAT @ cat @ VBS2004 |
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453 |
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Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich |
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Title |
Perception Based Representations for Computational Colour |
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Conference Article |
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Year |
2011 |
Publication |
3rd International Workshop on Computational Color Imaging |
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6626 |
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16-30 |
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Keywords |
colour perception, induction, naming, psychophysical data, saliency, segmentation |
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The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space. |
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Milan, Italy |
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Springer-Verlag |
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Raimondo Schettini, Shoji Tominaga, Alain Trémeau |
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978-3-642-20403-6 |
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CCIW |
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CIC |
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no |
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Admin @ si @ VMB2011 |
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1733 |
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Author |
Maria Vanrell; Jordi Vitria; Xavier Roca |
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Title |
A multidimensional scaling approach to explore the behavior of a texture perception algorithm. |
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Journal Article |
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Year |
1997 |
Publication |
Machine Vision and Applications |
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9 |
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262–271 |
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OR;ISE;CIC;MV |
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no |
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BCNPCL @ bcnpcl @ VVR1997 |
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35 |
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Author |
Maria Vanrell; Jordi Vitria; Xavier Roca |
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Title |
A General Morphological Framework for Perceptual Texture Discrimination based on Granulometries. |
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Miscellaneous |
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1993 |
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Technical Workshop on Mathematical Morphology and its Applications to Signal Processing. |
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Barcelona |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ VVR1993 |
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154 |
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Author |
Maria Vanrell; Jordi Vitria |
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Title |
Mathematical Morphology, Granulometries and Texture Perception. |
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Miscellaneous |
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1993 |
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SPIE International Symposium on Optical Instrumentation and Applied Science (Conference on image Algebra and Morphological image Processing IV). |
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San Diego; CA; USA |
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OR;CIC;MV |
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BCNPCL @ bcnpcl @ VaV1993 |
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178 |
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Author |
Maria Vanrell; Jordi Vitria |
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Title |
Optimal 3x3 decomposable disks for morphological transformations |
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1997 |
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Image and Vision Computing, 15(2): 845–854 |
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OR;CIC;MV |
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BCNPCL @ bcnpcl @ VaV1997c |
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543 |
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Author |
Maria Vanrell; Felipe Lumbreras; A. Pujol; Ramon Baldrich; Josep Llados; Juan J. Villanueva |
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Title |
Colour Normalisation Based on Background Information. |
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Miscellaneous |
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2001 |
Publication |
Proceeding ICIP 2001, IEEE International Conference on Image Processing |
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ICIP 2001 |
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1 |
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874–877 |
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Grecia. |
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ADAS;DAG;CIC |
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no |
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ADAS @ adas @ VLP2001 |
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167 |
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Maria Vanrell |
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Exploring the space of behaviour of a texture perception algorithm |
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1997 |
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CVC Technical Report #12 |
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CVC (UAB) |
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CIC |
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CAT @ cat @ Van1997 |
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523 |
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Maria Salamo; Sergio Escalera; Petia Radeva |
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Quality Enhancement based on Reinforcement Learning and Feature Weighting for a Critiquing-Based Recommender |
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2009 |
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8th International Conference on Case-Based Reasoning |
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5650 |
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298–312 |
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Personalizing the product recommendation task is a major focus of research in the area of conversational recommender systems. Conversational case-based recommender systems help users to navigate through product spaces, alternatively making product suggestions and eliciting users feedback. Critiquing is a common form of feedback and incremental critiquing-based recommender system has shown its efficiency to personalize products based primarily on a quality measure. This quality measure influences the recommendation process and it is obtained by the combination of compatibility and similarity scores. In this paper, we describe new compatibility strategies whose basis is on reinforcement learning and a new feature weighting technique which is based on the user’s history of critiques. Moreover, we show that our methodology can significantly improve recommendation efficiency in comparison with the state-of-the-art approaches. |
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Seattle, USA |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-02998-1 |
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ICCBR |
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HuPBA; MILAB |
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no |
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BCNPCL @ bcnpcl @ SER2009 |
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1187 |
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Author |
Maria Salamo; Sergio Escalera |
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Increasing Retrieval Quality in Conversational Recommenders |
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2011 |
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IEEE Transactions on Knowledge and Data Engineering |
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TKDE |
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99 |
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1-1 |
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IF JCR CCIA 2.286 2009 24/103
JCR Impact Factor 2010: 1.851
A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user’s soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns Turing the recommendation process, we propose two novel exponential reinforcement learning approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a global weighting approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches |
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IEEE |
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1041-4347 |
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MILAB; HuPBA |
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Admin @ si @ SaE2011 |
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1713 |
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Author |
Maria Salamo; Inmaculada Rodriguez; Maite Lopez; Anna Puig; Simone Balocco; Mariona Taule |
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Recurso docente para la atención de la diversidad en el aula mediante la predicción de notas |
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2016 |
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ReVision |
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9 |
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1 |
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Aprendizaje automatico; Sistema de prediccion de notas; Herramienta docente |
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Desde la implantación del Espacio Europeo de Educación Superior (EEES) en los diferentes grados, se ha puesto de manifiesto la necesidad de utilizar diversos mecanismos que permitan tratar la diversidad en el aula, evaluando automáticamente y proporcionando una retroalimentación rápida tanto al alumnado como al profesorado sobre la evolución de los alumnos en una asignatura. En este artículo se presenta la evaluación de la exactitud en las predicciones de GRADEFORESEER, un recurso docente para la predicción de notas basado en técnicas de aprendizaje automático que permite evaluar la evolución del alumnado y estimar su nota final al terminar el curso. Este recurso se ha complementado con una interfaz de usuario para el profesorado que puede ser usada en diferentes plataformas software (sistemas operativos) y en cualquier asignatura de un grado en la que se utilice evaluación continuada. Además de la descripción del recurso, este artículo presenta los resultados obtenidos al aplicar el sistema de predicción en cuatro asignaturas de disciplinas distintas: Programación I (PI), Diseño de Software (DSW) del grado de Ingeniería Informática, Tecnologías de la Información y la Comunicación (TIC) del grado de Lingüística y la asignatura Fundamentos de Tecnología (FDT) del grado de Información y Documentación, todas ellas impartidas en la Universidad de Barcelona.
La capacidad predictiva se ha evaluado de forma binaria (aprueba o no) y según un criterio de rango (suspenso, aprobado, notable o sobresaliente), obteniendo mejores predicciones en los resultados evaluados de forma binaria. |
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MILAB; |
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Admin @ si @ SRL2016 |
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2820 |
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Maria Oliver; Gloria Haro; Mariella Dimiccoli; Baptiste Mazin; Coloma Ballester |
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A computational model of amodal completion |
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Conference Article |
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2016 |
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SIAM Conference on Imaging Science |
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This paper presents a computational model to recover the most likely interpretation of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling. |
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Albuquerque; New Mexico; USA; May 2016 |
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MILAB; 601.235 |
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Admin @ si @OHD2016a |
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2788 |
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Author |
Maria Oliver; G. Haro; Mariella Dimiccoli; B. Mazin; C. Ballester |
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A Computational Model for Amodal Completion |
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Journal Article |
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2016 |
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Journal of Mathematical Imaging and Vision |
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JMIV |
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56 |
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3 |
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511–534 |
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Perception; visual completion; disocclusion; Bayesian model;relatability; Euler elastica |
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This paper presents a computational model to recover the most likely interpretation
of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth.
Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling. |
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MILAB; 601.235 |
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Admin @ si @ OHD2016b |
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2745 |
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