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Image Segmentation Algorithm of Regional Information Based on Generalization Ability

Xiliang Zeng, Dangui Chen

Abstract


This energy function is incorporated into the generalizing formulation with the generalizing fusion term that is necessary for accurate computation in the corresponding generalizing method. The energy function for the proposed model consists of overall option and regional option. Therefore, the combination of two terms allows for flexible initialization of the generalization abilities. In this paper, we propose a novel regional information model in the variation generalizing formulation. The proposed model is first presented as a two-term formulation and then extended to a multi-phase formulation. The experimental results on some synthetic and real images have demonstrated the efficiency and robustness of our model. Moreover, the comparisons with recent popular regional information fitting (RIF) model also show that our model is less sensitive to the location of initial region.

Keywords


overall option, regional option, generalization ability, image segmentation.

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