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一项关于用高斯和波桑噪声进行虹膜图像识别的中性学[公式:参见文本]-语义细分的研究

Vinoth Dhatchinamoorthy¹, Ezhilmaran Devarasan¹, Asima Razzaque^2,3

¹Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamilnadu, India.

Scientific reports

|April 21, 2025

概括

这项研究引入了一种用于图像细分的新型中性学集合方法,有效地处理不确定性. 该方法在虹膜图像上获得了85%的细分分数,证明了其可靠生物识别分析的潜力.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
人工智能的人工智能

背景情况:

图像细分对于图像分析至关重要,但经常与固有的不确定性作斗争.
中性学集合为管理数据的不确定性提供了一个强大的框架.
现有的方法可能无法充分解决多类细分的挑战.

研究的目的:

开发和评估使用中微镜集的创新图像细分方法.
通过分析图像不确定性来实现有效的多类细分.
为了证明这种方法在生物识别虹膜数据上的应用.

主要方法:

利用了具有真伪会员函数的1型中性学集合来进行细分.
根据图像强度值和中微镜成员函数进行了多类细分.
引入了来自CASIA V1数据库的虹膜图像的高斯式和波松式噪声,以进行可靠的分析.

主要成果:

取得了令人印象深刻的整体细分分数85%.
证明了该方法在处理杂的生物识别虹膜图像数据方面的有效性.
使用包括Jaccard,MIOU,精度,回忆,F1得分和准确性在内的指标评估性能.

结论:

拟议的中微镜集方法为图像细分提供了一种可靠的方法,特别是在存在不确定性的情况下.
该方法在生物识别,特别是虹膜细分方面的应用方面显示出重大前景.
需要进一步研究中微镜集在图像处理中的应用.

关键词:

虹膜识别功能虹膜识别功能中性人形生物识别.中性视觉计算机视觉 (neutrosophic computer vision) 是一种中性视觉计算机视觉技术.中性镜像处理图像处理.中性索性细分的细分.语义细分语义细分是指语义细分.

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