|
|
Ernest Valveny and Philippe Dosch. 2007. A General Framework for the Evaluation of Symbol Recognition Methods.
|
|
|
|
Oriol Ramos Terrades, Salvatore Tabbone and Ernest Valveny. 2007. Optimal Linear Combination for Two-class Classifiers. Proceedings of the International Conference on Advances in Pattern Recognition.
|
|
|
|
Mathieu Nicolas Delalandre, Ernest Valveny and Josep Llados. 2008. Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview.
|
|
|
|
Miquel Ferrer, Robert Benavente, Ernest Valveny, J. Garcia, Agata Lapedriza and Gemma Sanchez. 2008. Aprendizaje Cooperativo Aplicado a la Docencia de las Asignaturas de Programacion en Ingenieria Informatica.
|
|
|
|
Oriol Ramos Terrades, Ernest Valveny and Salvatore Tabbone. 2008. On the Combination of Ridgelets Descriptors for Symbol Recognition. Graphics Recognition: Recent Advances and New Oportunities, W. Lius, J. Llados, J.M. Ogier, LNCS 5046:104–113.
|
|
|
|
Liu Wenyin, Josep Llados and Jean-Marc Ogier. 2008. Graphics Recognition. Recent Advances and New Opportunities.. (LNCS.)
|
|
|
|
Robert Benavente, Ernest Valveny, Jaume Garcia, Agata Lapedriza, Miquel Ferrer and Gemma Sanchez. 2008. Una experiencia de adaptacion al EEES de las asignaturas de programacion en Ingenieria Informatica.
|
|
|
|
Alfons Juan-Ciscar and Gemma Sanchez. 2008. PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008.
|
|
|
|
Miquel Ferrer, Ernest Valveny, F. Serratosa, K. Riesen and Horst Bunke. 2008. An Approximate Algorith for Median Graph Computation using Graph Embedding. 19th International Conference on Pattern Recognition..
|
|
|
|
Dimosthenis Karatzas, Marçal Rusiñol, Coen Antens and Miquel Ferrer. 2008. Segmentation Robust to the Vignette Effect for Machine Vision Systems. 19th International Conference on Pattern Recognition.
Abstract: The vignette effect (radial fall-off) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, non-linear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect.
|
|
