Abstract
In this work, a computationally efficient segmentation framework is proposed. The proposed methodology is able to auto determine the optimal number of required thresholds in order to achieve a good and accurate segmentation. Atanassov's intuitionistic fuzzy sets and intuitionistic fuzzy entropy in particular, play an important role in the determination of both the threshold value and the number of required thresholds. Experimental results and their evaluation according to uniformity measures are presented. © 2010 Springer-Verlag.
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CITATION STYLE
Couto, P., Bustince, H., Pagola, M., Jurio, A., & Melo-Pinto, P. (2010). A-IFSs entropy based image multi-thresholding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6098 LNAI, pp. 341–349). Springer Verlag. https://doi.org/10.1007/978-3-642-13033-5_35
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