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中国科学院大学学报 ›› 2012, Vol. ›› Issue (3): 392-398.DOI: 10.7523/j.issn.2095-6134.2012.3.017

• 计算机科学 • 上一篇    下一篇

高分辨率捺印掌纹的自动分割

周雨阳1, 阿勇1, 吴敏2, 文成明1   

  1. 1. 中国科学院研究生院数学科学学院, 北京 100049;
    2. 中国科学院研究生院信息科学与工程学院, 北京 100049
  • 收稿日期:2011-04-01 修回日期:2011-04-19 发布日期:2012-05-15
  • 通讯作者: 周雨阳
  • 基金资助:
    国家自然科学基金(10831006)和中国科学院创新工程项目(kjcx-yw-s7)资助

Automatic high-resolution latent palmprint segmentation

ZHOU Yu-Yang1, A Yong1, WU Min2, WEN Cheng-Ming1   

  1. 1. School of Mathematical Sciences, Graduate University, Chinese Academy of Sciences, Beijing 100049, China;
    2. School of Information Science and Engineering, Graduate University, Chinese Academy of Sciences, Beijing 100049, China
  • Received:2011-04-01 Revised:2011-04-19 Published:2012-05-15

摘要: 设计了2种全新的自动掌纹分割算法. 第1种利用一致局部二进制模式的频数统计逐块检测背景区域;第2种根据相邻区域(块)之间的纹理差异容忍度,将相似的区域合二为一,差异较大的标记为两类区域.然后通过区域生长分别得到掌纹前景和背景.2种算法在标准掌纹档案库中都得到了成功应用.

关键词: 掌纹分割, 一致局部二进制模式, 区域生长

Abstract: High-resolution latent palmprint image is one of the primary forensic evidences. However the image is still manually segmented for its huge size and complex texture. Two automatic segmentation algorithms are proposed to solve this problem. The first detects the background based on the frequencies of ULBP in each block. The other compares the texture dissimilarity between neighbor regions in each couple until all the regions grow up into the foreground and background. The experiments on the standard latent palmprint database show effectiveness of the two algorithms.

Key words: palmprint segmentation, ULBP, region growing

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