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- W4383502023 abstract "The effectiveness of the FCM image segmentation algorithm can be increased by introducing local spatial information as FCM is very sensitive to noise. Also, during transmission, the image details are lost because noise is added. Thus, an Alpha-trimmed mean filter is utilized as a pre-processing step to maintain the image features and immunity. Then SSFCA algorithm is used to obtain a fuzzy membership with sparsity. It is used to reduce the non-homogeneous interference and got a good segmentation result in comparison to other self-optimized FCM algorithms. Again, to overcome the background noise the resultant image is applied to the morphological operators as a postprocessing step. Experiments show that this method can create good segmentations." @default.
- W4383502023 created "2023-07-08" @default.
- W4383502023 creator A5014283125 @default.
- W4383502023 creator A5060535483 @default.
- W4383502023 date "2023-05-26" @default.
- W4383502023 modified "2023-09-29" @default.
- W4383502023 title "Improved Self-Sparse Fuzzy Clustering Algorithm for Image Segmentation" @default.
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- W4383502023 doi "https://doi.org/10.1109/ic3s57698.2023.10169750" @default.
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