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An Efficient Two-Stage Level Set Segmentation Framework for Overlapping Plant Leaf Image

  • Xiao-Feng Wang
  • Hai Min
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

In this paper, an efficient two-stage segmentation framework was proposed to address the plant leaf image with overlapping phenomenon, which is built based on the leaf approximate symmetry and level set evolution theory. In the pre-segmentation stage, a straight line was manually set on the target leaf to approximate the principal leaf vein and the Local Chan-Vese (LCV) model was used on the global image region to help searching the so-called un-overlapping contour in target leaf. In the formal segmentation stage, the symmetry detection was performed based on the pre-defined approximated principal vein to obtain the narrow-band evolution region and the second initial contour. Next, the LCV model was once again used to find the complete target leaf contour in the narrow-band evolution region. Finally, experiments on some real leaf images with overlapping phenomenon have demonstrated the efficiency and robustness of the proposed segmentation framework.

Keywords

approximate symmetry level set overlapping leaf principal vein two-stage segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiao-Feng Wang
    • 1
    • 2
  • Hai Min
    • 2
    • 3
  1. 1.Key Lab of Network and Intelligent Information Processing, Department of Computer Science and TechnologyHefei UniversityHefeiChina
  2. 2.Intelligent Computing Lab, Hefei Institute of Intelligent MachinesChinese Academy of SciencesHefeiChina
  3. 3.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina

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