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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Main Authors: Hang Zhang, Jian Liu, Lin Chen, Ning Chen, Xiao Yang
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/19/15/3285
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spelling 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
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