Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images
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 pre...
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doaj-adfc08b13e0845f583d267f2f0b6770c2020-11-25T01:57:18ZengMDPI AGSensors1424-82202019-07-011915328510.3390/s19153285s19153285Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing ImagesHang Zhang0Jian Liu1Lin Chen2Ning Chen3Xiao Yang4State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha 410082, ChinaDue 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 <i>F</i> index can characterize the aliasing degree in the surface images and is highly correlated with surface roughness (<i>R</i><sup>2</sup> = 0.9327 for thirty grinding samples).https://www.mdpi.com/1424-8220/19/15/3285fuzzy clusteringimage segmentationspatial informationsurface roughness |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hang Zhang Jian Liu Lin Chen Ning Chen Xiao Yang |
spellingShingle |
Hang Zhang Jian Liu Lin Chen Ning Chen Xiao Yang Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images Sensors fuzzy clustering image segmentation spatial information surface roughness |
author_facet |
Hang Zhang Jian Liu Lin Chen Ning Chen Xiao Yang |
author_sort |
Hang Zhang |
title |
Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images |
title_short |
Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images |
title_full |
Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images |
title_fullStr |
Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images |
title_full_unstemmed |
Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images |
title_sort |
fuzzy clustering algorithm with non-neighborhood spatial information for surface roughness measurement based on the reflected aliasing images |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-07-01 |
description |
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 <i>F</i> index can characterize the aliasing degree in the surface images and is highly correlated with surface roughness (<i>R</i><sup>2</sup> = 0.9327 for thirty grinding samples). |
topic |
fuzzy clustering image segmentation spatial information surface roughness |
url |
https://www.mdpi.com/1424-8220/19/15/3285 |
work_keys_str_mv |
AT hangzhang fuzzyclusteringalgorithmwithnonneighborhoodspatialinformationforsurfaceroughnessmeasurementbasedonthereflectedaliasingimages AT jianliu fuzzyclusteringalgorithmwithnonneighborhoodspatialinformationforsurfaceroughnessmeasurementbasedonthereflectedaliasingimages AT linchen fuzzyclusteringalgorithmwithnonneighborhoodspatialinformationforsurfaceroughnessmeasurementbasedonthereflectedaliasingimages AT ningchen fuzzyclusteringalgorithmwithnonneighborhoodspatialinformationforsurfaceroughnessmeasurementbasedonthereflectedaliasingimages AT xiaoyang fuzzyclusteringalgorithmwithnonneighborhoodspatialinformationforsurfaceroughnessmeasurementbasedonthereflectedaliasingimages |
_version_ |
1724974976762642432 |