Abstract
In this paper we propose a genetic and greedy algorithm combination for the optimization of the Topological Active Nets (TAN) model. This is a deformable model used for image segmentation that integrates features of region-based and edge-based segmentation techniques, being able to fit the edges of the objects and model their inner topology. The hybrid approach we propose can optimize the active nets through the minimization of the model energy functions and, moreover, it can provide some segmentation results unreachable by the GA method alone such as changes in the net topology.
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Santos, J., Ibáñez, Ó., Barreira, N., Penedo, M.G. (2007). Genetic-Greedy Hybrid Approach for Topological Active Nets Optimization. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_23
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DOI: https://doi.org/10.1007/978-3-540-71618-1_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71589-4
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