A Novel Ni/YSZ Anode Image Segmentation Method for Solid Oxide Fuel Cell Electrodes Microstructure. Issue 6 (12th October 2016)

• Record Type: Journal Article
• Authors: Fu, X.; Xiang, Y.; Chen, L.; Xu, X.; Li, X.
• Is Part Of: Fuel cells. Volume 16:Issue 6(2016:Dec.)
• Page Start: 810
• Page End: 821
• Publication Date: 2016-10-12
• Subjects: Gaussian Mixture Model (GMM) -- Image Segmentation -- Markov Random Filed (MRF) -- Microstructure -- Solid Oxide Fuel Cell (SOFC); Fuel cells -- Periodicals; 621.312429
• DOI: 10.1002/fuce.201600065
• Languages: English
• ISSNs: 1615-6846

Abstract

Abstract: For the three‐phase identification of solid oxide cuel cell (SOFC) electrode, this paper presents a novel segmentation method based on gaussian mixture model (GMM) for YSZ/Ni anode optical microscopy (OM) images. A coarseness‐entropy adaptive factor is defined to incorporate the spatial information based on markov random field (MRF) into GMM. Furthermore, the proposed method can obtain the trade‐off between robustness to noise and effectiveness of preserving the details. Experimental results show that the proposed method outperforms the compared method on three‐phase microstructure identification. It can provide reliable foundation for the quantification of SOFC microstructure parameters.