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Face segmentation based on level set and improved DBM prior shape

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Abstract

This paper puts forward a new method of level set image segmentation based on prior shape, which aims to provide a better solution to the challenging segmentation problems that typically occur in images with complex background, intensity inhomogeneity and partially blocked targets. First, we introduced glial cells into deep Boltzmann machine (DBM) to solve that units in the DBM layer are not connected to each other, and then the novel DBM is employed to learn prior shape. Next, we used the variational level set and the local Gaussian distribution to fit the image energy term with local mean and local variance of image. Then, the prior shape energy is integrated into the image energy term to construct the final energy segmentation model. The experimental results show that the new model has stronger robustness and higher efficiency for face images segmentation.

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Acknowledgements

This Research is funded by the Education Department of Liaoning Province Foundation Grant Number LJQ2014033 and the Natural Science Foundation of Liaoning Province Grant Number 20180551048.

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Authors and Affiliations

  1. School of Software Engineering, University of Science and Technology Liaoning, No.185, Qianshanzhong Road, New and High-Tech Zone, Anshan, 114051, Liaoning Province, China

    Xiaoling Wu, Ji Zhao & Huibin Wang

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  1. Xiaoling Wu
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  2. Ji Zhao
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  3. Huibin Wang
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Correspondence to Ji Zhao.

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Wu, X., Zhao, J. & Wang, H. Face segmentation based on level set and improved DBM prior shape. Prog Artif Intell 8, 167–179 (2019). https://doi.org/10.1007/s13748-018-00169-5

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