arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1505.04585cs.CV
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基于三部分分解的全局变分指纹分割方法

Global Variational Method for Fingerprint Segmentation by Three-part Decomposition

University of Goettingen(哥廷根大学)

Duy Hoang Thai, Carsten Gottschlich

摘要:

通过指纹识别验证身份声明是数百万人在日常生活中的常见体验,例如用于解锁平板电脑或智能手机。在指纹图像采集之后的第一处理步骤是分割,即将指纹图像划分为包含用于比较算法的相关特征的前景区域和背景区域。本文提出了一种基于全局三部分分解(G3PD)的分割方法。基于全局变分分析的G3PD方法将指纹图像分解为卡通部分、纹理部分和噪声部分。分解后,前景区域是从非零系数的纹理图像中使用形态学处理。G3PD方法的分割性能进行比较,五个国家的最先进的方法的基准,其中包括手动标记的地面真值分割10560图像。性能评估表明,G3PD方法在分割精度方面始终优于现有方法。

英文摘要:

Verifying an identity claim by fingerprint recognition is a commonplace experience for millions of people in their daily life, e.g. for unlocking a tablet computer or smartphone. The first processing step after fingerprint image acquisition is segmentation, i.e. dividing a fingerprint image into a foreground region which contains the relevant features for the comparison algorithm, and a background region. We propose a novel segmentation method by global three-part decomposition (G3PD). Based on global variational analysis, the G3PD method decomposes a fingerprint image into cartoon, texture and noise parts. After decomposition, the foreground region is obtained from the non-zero coefficients in the texture image using morphological processing. The segmentation performance of the G3PD method is compared to five state-of-the-art methods on a benchmark which comprises manually marked ground truth segmentation for 10560 images. Performance evaluations show that the G3PD method consistently outperforms existing methods in terms of segmentation accuracy.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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