|
Joan Mas, B. Lamiroy, Gemma Sanchez and Josep Llados. 2006. Automatic Adjacency Grammar Generation from User Drawn Sketches.
|
|
|
Joan Mas, B. Lamiroy, Gemma Sanchez and Josep Llados. 2006. Automatic Learning of Symbol Descriptions Avoiding Topological Ambiguities.
|
|
|
Jose Antonio Rodriguez, Gemma Sanchez and Josep Llados. 2006. Automatic Interpretation of Proofreading Sketches.
|
|
|
Oriol Ramos Terrades, Salvatore Tabbone and Ernest Valveny. 2006. Combination of shape descriptors using an adaptation of boosting.
|
|
|
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.
|
|
|
Agata Lapedriza, Jaume Garcia, Ernest Valveny, Robert Benavente, Miquel Ferrer and Gemma Sanchez. 2008. Una experiencia de aprenentatge basada en projectes en el ambit de la informatica.
|
|
|
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.
|
|
|
Ernest Valveny, Robert Benavente, Agata Lapedriza, Miquel Ferrer, Jaume Garcia and Gemma Sanchez. 2012. Adaptation of a computer programming course to the EXHE requirements: evaluation five years later.
|
|
|
Carles Sanchez, Oriol Ramos Terrades, Patricia Marquez, Enric Marti, Jaume Rocarias and Debora Gil. 2014. Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías.
|
|
|
Sounak Dey, Anjan Dutta, Juan Ignacio Toledo, Suman Ghosh, Josep Llados and Umapada Pal. 2018. SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification.
Abstract: Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction.
|
|