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计算机科学

高分辨率捺印掌纹的自动分割

  • 周雨阳 ,
  • 阿勇 ,
  • 吴敏 ,
  • 文成明
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  • 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

  网络出版日期: 2012-05-15

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-Yang ,
  • A Yong ,
  • WU Min ,
  • WEN Cheng-Ming
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  • 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 date: 2011-04-01

  Revised date: 2011-04-19

  Online 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.

本文引用格式

周雨阳 , 阿勇 , 吴敏 , 文成明 . 高分辨率捺印掌纹的自动分割[J]. 中国科学院大学学报, 2012 , (3) : 392 -398 . DOI: 10.7523/j.issn.2095-6134.2012.3.017

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.

参考文献

[1] Jain A K, Ross A, Prabhakar S. An introduction to biometric recognition[J]. IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image-and Video-Based Bipmetrics, 2004, 14(1): 4-20.
[2] 张海国. 人类肤纹学[M]. 上海:上海交通大学出版社,2006: 75.
[3] Jain A K, Feng J. Latent palmprint matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(6): 1032-1047.
[4] Zheng Y, Liu Y, Shi G, et al. Segmentation of offline palmprintthe //Proceedings of the 3rd International IEEE Conference on Signal-Image Technologies and Internet-Based System. 2008: 804-811.
[5] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
[6] Wu M, Zhao T, Zhou Y Y, et al. A novel segmentation algorithm for fingerprint image based on region merging //IEEE, 2010 International Conference on Intelligent Systems Design and Engineering Application (ISDEA 2010). Changsha, China, 2010: 124-128.
[7] Burrus N, Bernard T, Jolion J. Image segmentation by a contrario simulation[J]. Pattern Recognition, 2009, 42(7): 1520-1532.
[8] Zhou Y Y, Guo T D, Wu M, et al. Latent palmprint image segmentation based on dissimilarity tolerance //Proceedings of IEEE International Conference on Multimedia Communications(Mediacom 2010). HongKong, 2010: 83-86.
[9] Wen C M, Guo T D, Zhou Y Y. A novel and efficient algorithm for segmentation of fingerprint image based on LBP operator //2009 International Conference on Information Technology and Computer Science (ITCS 2009). Kiev, Ukraine, 2009, 2: 200-204.
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