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Robust EM Algorithm for Iris Segmentation Based on Mixture of Gaussian Distribution

机译:基于高斯分布混合的鲁棒EM虹膜分割算法。

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摘要

Density estimation via Gaussian mixture modelling has been successfully applied to image segmentation. In this paper, we have learned distributions mixture model to the pixel of an iris image as training data. We introduce the proposed algorithm by adapting the Expectation-Maximization (EM) algorithm. To further improve the accuracy for iris segmentation, we consider the EM algorithm in Markovian and non Markovian cases. Simulated data proves the accuracy of our algorithm. The proposed method is tested on a subset of the CASIA database by Chinese Academy of Sciences Institute of Automation-Iris-Twins. The obtained results have shown a significant improvement of our approach compared to the standard version of EM algorithm and the classical segmentation method.
机译:通过高斯混合建模的密度估计已成功地应用于图像分割。在本文中,我们学习了将混合模型分布到虹膜图像的像素作为训练数据。我们通过适应期望最大化(EM)算法来介绍该算法。为了进一步提高虹膜分割的准确性,我们在马尔可夫和非马尔可夫情况下考虑了EM算法。仿真数据证明了该算法的准确性。中国科学院自动化研究所-Iris-Twins在CASIA数据库的子集上对提出的方法进行了测试。与标准版本的EM算法和经典分割方法相比,获得的结果表明我们的方法有了显着改进。

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