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A Fast Region‐Based Segmentation Model with Gaussian Kernel of Fractional Order

Author

Listed:
  • Bo Chen
  • Qing-Hua Zou
  • Wen-Sheng Chen
  • Yan Li

Abstract

By summarizing some classical active contour models from the view of level set representation, a simple energy function expression with the Gaussian kernel of fractional order is proposed, and then a novel region‐based geometric active contour model is established. In this proposed model, the energy function with value of [−1, 1] is built, the local mean and global mean of the inside and outside of the evolution curve are employed, and the segmentation results are obtained by controlling the expansion and contraction of the evolution curve. The model is simple and easy to implement; it can also protect weak edges because of considering more statistical information. Experimental results on synthetic and natural images show that the proposed model is much more effective in dealing with the images with weak or blurred edges, and it takes less time.

Suggested Citation

  • Bo Chen & Qing-Hua Zou & Wen-Sheng Chen & Yan Li, 2013. "A Fast Region‐Based Segmentation Model with Gaussian Kernel of Fractional Order," Advances in Mathematical Physics, John Wiley & Sons, vol. 2013(1).
    • Handle: RePEc:wly:jnlamp:v:2013:y:2013:i:1:n:501628
    DOI: 10.1155/2013/501628
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    References listed on IDEAS

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    1. Li, Ming & Zhao, Wei, 2012. "Quantitatively investigating the locally weak stationarity of modified multifractional Gaussian noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6268-6278.
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