Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Automatic 3D Shape Reconstruction of Bones Using Active Nets Based Segmentation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Global Optimization of Deformable Surface Meshes Based on Genetic Algorithms
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Genetic-Greedy Hybrid Approach for Topological Active Nets Optimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Automatic Topological Active Net Division in a Genetic-Greedy Hybrid Approach
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Optic Disc Segmentation by Means of GA-Optimized Topological Active Nets
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Genetic approaches for topological active nets optimization
Pattern Recognition
Localisation of the optic disc by means of GA-optimised Topological Active Nets
Image and Vision Computing
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The Topological Active Net (TAN) model is a deformable model used for image segmentation. It integrates features of region–based and edge–based segmentation techniques. This way, the model is able to fit the edges of the objects and model their inner topology. The model consists of a two dimensional mesh controlled by energy functions. The minimization of these energy functions leads to the TAN adjustment. This paper presents a new approach to the energy minimization process based on genetic algorithms (GA), that defines several suitable genetic operators for the optimization task. The results of the new GA approach are compared to the results of a greedy algorithm developed for the same task.