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Robust Edge-Based Model with Sparsity Representation for Object Segmentation

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10636))

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Abstract

Active contour models (ACM) based on level set method (LSM) are widely used in image segmentation. However, the classical edge-based models always extract some unnecessary objects or noise, and they lack robustness in segmenting weak boundary. In this paper, a method to constrain the evolution of contour is proposed. Firstly, extracting objects with a known topology (such as k connected objects) is viewed a sparse representation problem under a set of basis functions. According to sparse representation, a set of basis function is obtained with label operator to represent every connected region. Then, the corresponding energy functional model which views noise and non-objects as redundancy is defined based on basis functions. Furthermore, through the defined basis functions, a novel edge-stop term is designed and integrated into geometric active contour models. Experiments demonstrate that the proposed method improves the robust performances of ACM. On the other hand, the proposed method does not introduce any extra parameter.

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Acknowledgement

This work is jointly supported by the National Natural Science Foundation of China (No. U1404603).

Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, Henan, China

    Guoqi Liu & Chenjing Li

  2. College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, Henan, China

    Haifeng Li

Authors
  1. Guoqi Liu
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  2. Haifeng Li
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  3. Chenjing Li
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Corresponding author

Correspondence to Haifeng Li .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Liu, G., Li, H., Li, C. (2017). Robust Edge-Based Model with Sparsity Representation for Object Segmentation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_46

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  • DOI: https://doi.org/10.1007/978-3-319-70090-8_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70089-2

  • Online ISBN: 978-3-319-70090-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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