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Image segmentation based on super-pixel with neighborhood constrained and multi-level FCM clustering.
- Source :
- Journal of Computational Methods in Sciences & Engineering; 2022, Vol. 22 Issue 4, p1117-1130, 14p
- Publication Year :
- 2022
-
Abstract
- In order to solve the problem of noise sensitivity and low precision in image segmentation, a new image segmentation method based on super-pixel with neighborhood constrained and multi-level FCM clustering is proposed. Firstly, an improved SLIC algorithm of FCM is designed based on the local pixel region of the image to get the refined super-pixel. Then, the improved FCM clustering algorithm is combined with the spatial relationship of neighborhood super-pixel to mark the super-pixel categories. In order to verify the effectiveness of the algorithm, the artificial synthetic image with noise and natural image without noise are used for comparison experiments. The experimental result show the algorithm proposed in this paper can not only suppress the noise, but also improve the processing accuracy. [ABSTRACT FROM AUTHOR]
- Subjects :
- IMAGE segmentation
PIXELS
PROBLEM solving
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 14727978
- Volume :
- 22
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Journal of Computational Methods in Sciences & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 158043462
- Full Text :
- https://doi.org/10.3233/JCM-226001