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Open AccessArticle

Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images

State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha 410082, China
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Sensors 2019, 19(15), 3285; https://doi.org/10.3390/s19153285
Received: 29 May 2019 / Revised: 20 July 2019 / Accepted: 23 July 2019 / Published: 26 July 2019
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing 2019)
Due to the limitation of the fixed structures of neighborhood windows, the quality of spatial information obtained from the neighborhood pixels may be affected by noise. In order to compensate this drawback, a robust fuzzy c-means clustering with non-neighborhood spatial information (FCM_NNS) is presented. Through incorporating non-neighborhood spatial information, the robustness performance of the proposed FCM_NNS with respect to the noise can be significantly improved. The results indicate that FCM_NNS is very effective and robust to noisy aliasing images. Moreover, the comparison of other seven roughness indexes indicates that the proposed FCM_NNS-based F index can characterize the aliasing degree in the surface images and is highly correlated with surface roughness (R2 = 0.9327 for thirty grinding samples). View Full-Text
Keywords: fuzzy clustering; image segmentation; spatial information; surface roughness fuzzy clustering; image segmentation; spatial information; surface roughness
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Zhang, H.; Liu, J.; Chen, L.; Chen, N.; Yang, X. Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images. Sensors 2019, 19, 3285.

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