Abstract
This paper proposes a structure-preserving region model for machine images. Under the Bayesian framework, the proposed model is combined with MRF (Markov random field) to offer a new method for the segmentation of machine images. The structure-preserving region model aims to deal with problems with MRF-based segmentation on parameter estimation and optimization. Construction of the structure-preserving region model involves two processes. The bilateral filter algorithm is first applied to machine images to remove noise and restore image structures, followed by an initial segmentation by applying MRF on the images and represented by a region adjacency graph (RAG). The proposed segmentation method has been evaluated using machine images. Relative to existing MRF-based methods, testing results have demonstrated that our proposed method substantially improves the segmentation performance.
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Acknowledgements
This research was financially supported by Excellent Young Talents Fund Program of Higher Education Institutions of Anhui Province (Grant: gxyq2017049), the Foundation of Hefei Normal University (Grant: 2015JG05).
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Fan, C., Wang, Q. Research on image segmentation method using a structure-preserving region model-based MRF. Cluster Comput 22 (Suppl 6), 15329–15334 (2019). https://doi.org/10.1007/s10586-018-2592-2
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DOI: https://doi.org/10.1007/s10586-018-2592-2