Papers by Sebastia Massanet
Lecture Notes in Computer Science, 2015
Progress in Artificial Intelligence, 2015
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015
Springer Handbook of Computational Intelligence, 2015
Lecture Notes in Computer Science, 2015
IEEE Transactions on Fuzzy Systems, 2015
ABSTRACT In this paper, the fuzzy morphological gradients from the fuzzy mathematical morphologie... more ABSTRACT In this paper, the fuzzy morphological gradients from the fuzzy mathematical morphologies based on t-norms and conjunctive uninorms are deeply analyzed in order to establish which pair of conjunction and fuzzy implications are optimal, in accordance with their performance in edge detection applications. A novel three-step algorithm based on the fuzzy morphology is proposed. The comparison is performed by means of the so-called Pratt's figure of merit. In addition, a statistical analysis is carried out to study the relationship between the different configurations and to establish a classification of the conjunctions and implications considered. Both the objective measure and the statistical analysis conclude that the pairs nilpotent minimum t-norm and the Kleene–Dienes implication, and the idempotent uninorm obtained with the classical negation as a generator and its residual implication, are the best configurations in this approach, because they also obtain competitive results with respect to other approaches.
Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology, 2015
Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology, 2015
Lecture Notes in Computational Vision and Biomechanics, 2013
ABSTRACT Image edge detection is one of the more fashionable topics in image processing and it is... more ABSTRACT Image edge detection is one of the more fashionable topics in image processing and it is an important preprocessing step in many image processing techniques since its performance is crucial for the results obtained by subsequent higher-level processes. In this paper, an edge detection algorithm for noisy images, corrupted with salt and pepper noise, using a fuzzy morphology based on discrete t-norms is proposed. It is shown that this algorithm is robust when it is applied to different types of noisy images. The obtained results are objectively compared with other well-known morphological algorithms such as the ones based on the Łukasiewicz t-norm, representable and idempotent uninorms and the classical umbra approach. This comparison is addressed using some different objective measures for edge detection, such as Pratt’s figure of merit, the \(\rho \)-coefficient, and noise removal like the structural similarity index and the fuzzy \(DI\)-subsethood measure. The filtered results and the edge images obtained with our approach improve the values obtained by the other approaches.
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Papers by Sebastia Massanet