1. Interval possibilistic C-means algorithm and its application in image segmentation.
- Author
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Zeng, Wenyi, Liu, Yuqing, Cui, Hanshuai, Ma, Rong, and Xu, Zeshui
- Subjects
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IMAGE segmentation , *FACE perception , *ALGORITHMS , *NATURAL numbers , *TRAFFIC engineering , *FEATURE extraction - Abstract
Currently, image segmentation is widely used in face recognition, medical imaging, traffic control systems, and many other fields. The traditional possibilistic C-means (PCM) algorithm reduces the impact of outliers and noise on the computation of clustering centers; however, the clustering results are still poor due to high noise points. In this paper, an interval possibilistic C-means (IVPCM) algorithm is proposed that expands the natural number of image pixels to an interval value. In addition, a method to calculate the sample distance is proposed. Then, secondary feature extraction of interval values is conducted to improve the clustering. Several experiments show that IVPCM has more advantages in segmentation performance. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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