Abstract
We developed a novel method for image segmentation using deformable models. The deformable model adjustment is controlled by an Artificial Neural Network (ANN), which defines the deformations of the segmentation model through time. As deformable model we used Topological Active Nets, model which integrates features of region-based and boundary-based segmentation techniques. The evolved Artificial Neural Network learns to move each node of the segmentation model based on its energy surrounding. The ANN is applied to each of the nodes and in different temporal steps until the final segmentation is obtained. The ANN training is automatically obtained by simulated evolution, using differential evolution. This way, segmentation is an emergent process, result of the small deformations in the active model elements and through time. The new proposal was tested in different artificial and real images, showing the capabilities of the methodology.
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Acknowledgments
This paper has been partly funded by the Ministry of Science and Innovation through grant contracts TIN2011-25476 and TIN2011-27294 and by the Consellería de Industria, Xunta de Galicia through grant contract 10/CSA918054PR.
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Novo, J., Sierra, C.V., Santos, J., Penedo, M.G. (2014). Emergent Image Segmentation by Means of Evolved Connectionist Models and Using a Topological Active Net Model. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2013. Communications in Computer and Information Science, vol 449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44440-5_9
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DOI: https://doi.org/10.1007/978-3-662-44440-5_9
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