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

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3285 ◽  
Author(s):  
Hang Zhang ◽  
Jian Liu ◽  
Lin Chen ◽  
Ning Chen ◽  
Xiao Yang
Keyword(s):  
Surface Roughness ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Fuzzy Clustering Algorithm ◽  
Fuzzy C Means Clustering ◽  
Highly Correlated

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).

scholarly journals A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation

2021 ◽  
Author(s):  
Qiuyu Song ◽  
Chengmao Wu ◽  
Xiaoping Tian ◽  
Yue Song ◽  
Xiaokang Guo
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Local Information ◽  
Cluster Center ◽  
Fuzzy Clustering Algorithm ◽  
Robust Segmentation ◽  
Non Local ◽  
Self Learning

AbstractFuzzy clustering algorithm (FCM) can be directly used to segment images, it takes no account of the neighborhood information of the current pixel and does not have a robust segmentation noise suppression. Fuzzy Local Information C-means Clustering (FLICM) is a widely used robust segmentation algorithm, which combines spatial information with the membership degree of adjacent pixels. In order to further improve the robustness of FLICM algorithm, non-local information is embedded into FLICM algorithm and a fuzzy C-means clustering algorithm has local and non-local information (FLICMLNLI) is obtained. When calculating distance from pixel to cluster center, FLICMLNLI algorithm considers two distances from current pixel and its neighborhood pixels to cluster center. However, the algorithm gives the same weight to two different distances, which incorrectly magnifies the importance of neighborhood information in calculating the distance, resulting in unsatisfactory image segmentation effects and loss of image details. In order to solve this problem, we raise an improved self-learning weighted fuzzy algorithm, which directly obtains different weights in distance calculation through continuous iterative self-learning, then the distance metric with the weights obtained from self-learning is embedded in the objective function of the fuzzy clustering algorithm in order to improve the segmentation performance and robustness of the algorithm. A large number of experiments on different types of images show that the algorithm can not only suppress the noise but also retain the details in the image, the effect of segmenting complex noise images is better, and it provides better image segmentation results than the existing latest fuzzy clustering algorithms.

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