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Tire impressions image segmentation algorithm based on C-V model without re-initialization

  • Wang Zhen
  • , Wang Yunpeng*
  • , Li Shiwu
  • *Corresponding author for this work
  • Jilin University
  • Criminal Investigation Police University of China

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we present a new tire impressions image segmentation algorithm based on C-V model without re-initialization by introducing an internal energy term that penalizes the deviation of the level set function from a signed distance function into the C-V model. The proposed model can keep the approximately the level set function as a signed distance function during the curve evolution. The level set function can be initialized with general functions that are more efficient to construct and easier to use than the widely used signed distance function in practice and speed up the curve evolution. Therefore, the consuming time to compute a signed distance function from an initial curve in irregular shape is saved. The proposed algorithm has been applied to both printing and collected tire impressions images in the scene with promising results.

Original languageEnglish
Title of host publication2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011
Pages541-545
Number of pages5
DOIs
StatePublished - 2011
Event2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011 - Xi'an, China
Duration: 27 May 201129 May 2011

Publication series

Name2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011

Conference

Conference2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011
Country/TerritoryChina
CityXi'an
Period27/05/1129/05/11

Keywords

  • C-V model
  • Level set
  • Signed distance function
  • image segmentation
  • tire impressions

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