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International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition

EMMCVPR 2015: Energy Minimization Methods in Computer Vision and Pattern Recognition pp 436–449Cite as

Marked Point Process Model for Curvilinear Structures Extraction

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8932))

Abstract

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.

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Author information

Authors and Affiliations

  1. Ayin team, Inria, 2004 Route des Lucioles, 06902, Sophia-Antipolis, France

    Seong-Gyun Jeong, Yuliya Tarabalka & Josiane Zerubia

Authors
  1. Seong-Gyun Jeong
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  2. Yuliya Tarabalka
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  3. Josiane Zerubia
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Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Bergen, 7800, Bergen, Norway

    Xue-Cheng Tai

  2. Department of Mathematics, University of California, Los Angeles, CA, USA

    Egil Bae

  3. The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, S.A.R.

    Tony F. Chan

  4. Telemark University College, Postboks 203, 3901, Porsgrunn, Norway

    Marius Lysaker

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© 2015 Springer International Publishing Switzerland

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Jeong, SG., Tarabalka, Y., Zerubia, J. (2015). Marked Point Process Model for Curvilinear Structures Extraction. In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, Cham. https://doi.org/10.1007/978-3-319-14612-6_32

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  • DOI: https://doi.org/10.1007/978-3-319-14612-6_32

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14611-9

  • Online ISBN: 978-3-319-14612-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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