Marked Point Process Model for Curvilinear Structures Extraction

  • Seong-Gyun Jeong
  • Yuliya Tarabalka
  • Josiane Zerubia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8932)


In this paper, we propose a new marked point process (MPP) model and the associated optimization technique to extract curvilinear structures. Given an image, we compute the intensity variance and rotated gradient magnitude along the line segment. We constrain high level shape priors of the line segments to obtain smoothly connected line configuration. The optimization technique consists of two steps to reduce the significance of the parameter selection in our MPP model. We employ Monte Carlo sampler with delayed rejection to collect line hypotheses over different parameter spaces. Then, we maximize the consensus among line detection results to reconstruct the most plausible curvilinear structures without parameter estimation process. Experimental results show that the algorithm effectively localizes curvilinear structures on a wide range of datasets.


curvilinear structure extraction marked point process Monte Carlo sampling with delayed rejection aggregation algorithm 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Seong-Gyun Jeong
    • 1
  • Yuliya Tarabalka
    • 1
  • Josiane Zerubia
    • 1
  1. 1.Ayin teamInriaSophia-AntipolisFrance

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