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Journal of Mathematical Imaging and Vision

, Volume 60, Issue 1, pp 1–17 | Cite as

Image Segmentation with Depth Information via Simplified Variational Level Set Formulation

  • Lu Tan
  • Zhenkuan Pan
  • Wanquan LiuEmail author
  • Jinming Duan
  • Weibo Wei
  • Guodong Wang
Article

Abstract

Image segmentation with depth information can be modeled as a minimization problem with Nitzberg–Mumford–Shiota functional, which can be transformed into a tractable variational level set formulation. However, such formulation leads to a series of complicated high-order nonlinear partial differential equations which are difficult to solve efficiently. In this paper, we first propose an equivalently reduced variational level set formulation without using curvatures by taking level set functions as signed distance functions. Then, an alternating direction method of multipliers (ADMM) based on this simplified variational level set formulation is designed by introducing some auxiliary variables, Lagrange multipliers via using alternating optimization strategy. With the proposed ADMM method, the minimization problem for this simplified variational level set formulation is transformed into a series of sub-problems, which can be solved easily via using the Gauss–Seidel iterations, fast Fourier transform and soft thresholding formulas. The level set functions are treated as signed distance functions during computation process via implementing a simple algebraic projection method, which avoids the traditional re-initialization process for conventional variational level set methods. Extensive experiments have been conducted on both synthetic and real images, which validate the proposed approach, and show advantages of the proposed ADMM projection over algorithms based on traditional gradient descent method in terms of computational efficiency.

Keywords

Segmentation with depth Nitzberg–Mumford–Shiota (NMS)functional Variational level set formulation Alternating direction method of multipliers (ADMM) Projection method Fast Fourier Transform (FFT) Soft thresholding formulas 

Notes

Acknowledgements

The work has been partially supported by the National Natural Science Foundation of China with Grant numbers 61305045, 61170106, 61363066 and 61303079.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.College of Information EngineeringQingdao UniversityQingdaoChina
  2. 2.School of Computer ScienceUniversity of NottinghamNottinghamUK
  3. 3.Department of ComputingCurtin UniversityBentleyAustralia

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