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Progress in Artificial Intelligence
Erschienen in: Progress in Artificial Intelligence 2/2019

01.01.2019 | Regular Paper

Face segmentation based on level set and improved DBM prior shape

verfasst von: Xiaoling Wu, Ji Zhao, Huibin Wang

Erschienen in: Progress in Artificial Intelligence | Ausgabe 2/2019

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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.
Vorheriger Artikel A review on the self and dual interactions between machine learning and optimisation
Nächster Artikel Granular-based decomposition of complex fuzzy context and its analysis

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Metadaten
Titel
Face segmentation based on level set and improved DBM prior shape
verfasst von
Xiaoling Wu
Ji Zhao
Huibin Wang
Publikationsdatum
01.01.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Progress in Artificial Intelligence / Ausgabe 2/2019
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-018-00169-5

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