Multiobjective Genetic Algorithm for Image Thresholding

机译：用于图像阈值的多目标遗传算法

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In this paper we present a new image thresholding method based on a multiobjective Genetic Algorithm using the Pareto optimality approach. We aim to optimize multiple criteria in order to increase the segmentation quality. Thus, we've adapted the well-known Non Domination Sorting Genetic Algorithm [1] for this purpose so that it takes into consideration the contribution of the objective functions in improving the reproduction step and then improving the optimal Pareto front of solutions. Our method was tested against NSGAII algorithm and has shown effectiveness and convergence speed.

机译：在本文中，我们使用Pareto Optimaly方法提出了一种基于多目标遗传算法的新图像阈值方法。我们的目标是优化多个标准，以增加分割质量。因此，我们为此调整了众所周知的非统治分类遗传算法[1]，以便考虑到客观功能在改善再现步骤时的贡献，然后改善解决方案的最佳静脉前部。我们对NSGaii算法进行了测试的方法，并显示了有效性和收敛速度。

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《International Conference on Image and Signal Processing》|2012年||共10页
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Layla Tahri; Mohamed Wakrim;
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中图分类 TP3-53;
关键词
Evolutionary approach; Genetic algorithms; Image segmentation; Image thresholding; Multiobjective optimization; Pareto optimization;

机译：进化方法;遗传算法;图像分割;图像阈值;多目标优化;帕枪优化;

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Multiobjective Genetic Algorithm for Image Thresholding

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