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

Sensors

Ning Chen¹  Lin Chen¹  Hang Zhang¹  Xiao Yang¹  Jian Liu¹ 

[1] State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha 410082, China;

关键词: fuzzy clustering;    image segmentation;    spatial information;    surface roughness;   

DOI  :  10.3390/s19153285

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown