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Genetic approaches for topological active nets optimization
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DOI：10.1016/j.patcog.2008.09.005.png)
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The topological active nets (TANs) model is a deformable model used for image segmentation. It integrates features of region-based and edge-based segmentation techniques so it is able to Fit the contours of the objects and model their inner topology. Also, topological changes in its structure allow the detection of concave and convex contours, holes, and several objects in the scene. Since the model deformation is based on the minimization of an energy functional, the adjustment depends on the minimization algorithm. This paper presents two evolutionary approaches to the energy minimization problem in the TAN model. The first proposal is a genetic algorithm with ad hoc operators whereas the second approach is a hybrid model that combines genetic and greedy algorithms. Both evolutionary approaches improve the accuracy of the segmentation even though only the hybrid model allows topological changes in the model structure. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
Active nets
Topological active nets
Genetic algorithms
Hybrid optimization algorithms
Lamarckian strategy
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7.6
论文数:
1.3W
被引数:
4.5W
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