Abstract
In image segmentation tasks, shadow, clutter background and various interference factors in the image increase the difficulty of segmentation, and lead to unsatisfied results. To solve these problems, this paper proposes an image segmentation algorithm combining MRF (Markov Random Field) with deep learning shape priori. The target shape priori information is modelled and generated from deep learning models: Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), and Deep the Boltzmann Machine (DBM). Then the shape priori information is further defined and added to the energy function of the MRF image segmentation algorithm. Since the shape priori restricts the target shape and restrains the interference factors, better segmentation results are obtained. The proposed method is compared with traditional MRF and some comparable image segmentation methods in two datasets, the experiments results demonstrate the effective of the proposed method.
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Wang, Y., Wang, X. (2018). Image Segmentation Based on MRF Combining with Deep Learning Shape Priors. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_21
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DOI: https://doi.org/10.1007/978-3-319-97310-4_21
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