Abstract
With many techniques and new concepts that use energy minimization in edge-based Active Contour Models, the accurate segmentation is of great interest. However, most existing models are not accurate to capture uncertainty and vagueness appearing around object boundaries. In this work, we developed a model for image segmentation based on an active contour model using fuzzy logic and multi-agent system. We define Fuzzy sets using membership values assigned to local and global features of the active contour points. The parallel agents are employed to define a mapping to crisp sets using the information contained in the fuzzy set. The model is evaluated in several images and compared with other fuzzy ACMs in the literature. The results show that the proposed model can distinguish between noise, background and objects of interest whose boundaries are not necessarily defined by the gradient. In addition, better performance in optimizing runtime is obtained for several tests.
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Nachour, A., Ouzizi, L., Aoura, Y. (2018). Fuzzy Logic and Multi-agent for Active Contour Models. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016. AECIA 2016. Advances in Intelligent Systems and Computing, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-60834-1_24
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DOI: https://doi.org/10.1007/978-3-319-60834-1_24
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