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Marçal Rusiñol, & Lluis Gomez. (2018). Avances en clasificación de imágenes en los últimos diez años. Perspectivas y limitaciones en el ámbito de archivos fotográficos históricos. Revista anual de la Asociación de Archiveros de Castilla y León, 161–174.
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Marçal Rusiñol, R.Roset, Josep Llados, & C.Montaner. (2011). Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation. ePER - e-Perimetron, 219–229.
Abstract: By means of computer vision algorithms scanned images of maps are processed in order to extract relevant geographic information from printed coordinate pairs. The meaningful information is then transformed into georeferencing information for each single map sheet, and the complete set is compiled to produce a graphical index sheet for the map series along with relevant metadata. The whole process is fully automated and trained to attain maximum effectivity and throughput.
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Marc Sunset Perez, Marc Comino Trinidad, Dimosthenis Karatzas, Antonio Chica Calaf, & Pere Pau Vazquez Alcocer. (2016). Development of general‐purpose projection‐based augmented reality systems. IADIs - IADIs international journal on computer science and information systems, 1–18.
Abstract: Despite the large amount of methods and applications of augmented reality, there is little homogenizatio n on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more co ncerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection – based AR. The developed framework is suitable and has been tested in AR applications using camera – projector pairs, for both fixed and nomadic setups
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Maria Salamo, Inmaculada Rodriguez, Maite Lopez, Anna Puig, Simone Balocco, & Mariona Taule. (2016). Recurso docente para la atención de la diversidad en el aula mediante la predicción de notas. ReVision.
Abstract: 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.
Keywords: Aprendizaje automatico; Sistema de prediccion de notas; Herramienta docente
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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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Maria Vanrell, Ramon Baldrich, Anna Salvatella, Robert Benavente, & Francesc Tous. (2004). Induction operators for a computational colour-texture representation. Computer Vision and Image Understanding, 94(1–3):92–114, ISSN: 1077–3142 (IF: 0.651).
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Mariella Dimiccoli. (2016). Fundamentals of cone regression. Journal of Statistics Surveys, 53–99.
Abstract: 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.
Keywords: cone regression; linear complementarity problems; proximal operators.
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Matthias S. Keil. (2006). Smooth Gradient Representations as a Unifying Account of Chevreul’s Illusion, Mach Bands, and a Variant of the Ehrenstein Disk. NEURALCOMPUT - Neural Computation, 871–903.
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Matthias S. Keil, Agata Lapedriza, David Masip, & Jordi Vitria. (2008). Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine. PLoS ONE 3(7):e2590, DOI:10.1371/journal.pone.0002590.
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Matthias S. Keil, & Gabriel Cristobal. (2000). Separating the chaff from the wheat: possible origins of the oblique effect. Journal of the Optical Society of America A – Optics, Image Science, and Vision, 17(4): 697–710 (IF: 1.481).
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Matthias S. Keil, Gabriel Cristobal, & Heiko Neumann. (2006). Gradient representation and perception in the early visual system – A novel account of Mach band formation. VR - Vision Research, 46(17): 2659–2674.
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Matthias S. Keil, Gabriel Cristobal, Thorsten Hansen, & Heiko Neumann. (2005). Recovering real-world images from single-scale boundaries with a novel filling-in architecture. Neural Networks 18(10):1319–1331 (IF: 1.665).
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Matthias S. Keil, & Jordi Vitria. (2007). Pushing it to the Limit: Adaptation with Dynamically Switching Gain Control. EURASIP Journal on Advances in Signal Processing, Vol 2007, Article ID 51684, 10 pages, doi: 10.1155/2007/51684.
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Matthias S. Keil, & Jordi Vitria. (2005). Does the brain generate representations of smooth brightness gradients? A novel account for Mach bands, Chevreul’s illusion, and a variant of the Ehrenstein disk. Perception 34:209–210 Suppl. S (IF: 1.391).
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Mikhail Mozerov, Ariel Amato, Xavier Roca, & Jordi Gonzalez. (2009). Solving the Multi Object Occlusion Problem in a Multiple Camera Tracking System. Pattern Recognition and Image Analysis, 165–171.
Abstract: An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path.
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