Abstract
A new fuzzy edge detector based on uninorms is proposed and deeply studied. The behaviour of different classes of uninorms is discussed. The obtained results suggest that the best uninorm in order to improve the edge detection process is the uninorm \(\mathcal{U}_{\rm{min}}\), with underlying Łukasiewicz operators. This algorithm gets statistically substantial better results than the others obtained by well known edge detectors, as Sobel, Roberts and Prewitt approaches and comparable to the results obtained by Canny.
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González-Hidalgo, M., Massanet, S., Mir, A., Ruiz-Aguilera, D. (2014). A New Edge Detector Based on Uninorms. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 443. Springer, Cham. https://doi.org/10.1007/978-3-319-08855-6_19
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DOI: https://doi.org/10.1007/978-3-319-08855-6_19
Publisher Name: Springer, Cham
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