基于蚁群算法的多模态图像配准

Hadi Rezaei, M. Shakeri, S. Azadi, Keyvan Jaferzade
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引用次数: 7

摘要

图像配准是确定几何变换,将物体的一个图像中的点与另一个图像中的相应点对齐。为了找到最佳的变换函数,我们需要对相似性度量进行优化。优化方法一般分为全局方法和局部方法两大类。局部方法的问题在于它们会陷入局部最优。因此,本文采用蚁群算法作为一种基于真实蚂蚁行为的全局优化方法。实验结果表明,该方法比局部方法更有效,精度更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodality Image Registration Utilizing Ant Colony Algorithm
Image Registration is the determination of a geometrical transformation that aligns points in one image of an object with corresponding points in another image. To find the best transformation function we should optimize the similarity measure. The optimization methods are generally divided into two general classes of Global and Local methods. The problem with local methods is that they trap in local optima. So, in this paper we use Ant Colony Algorithm as a global optimization method which is based on real ant behavior. The results of our experiments show the effectiveness and better accuracy for this method rather than local methods.
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