Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
390883 | Fuzzy Sets and Systems | 2010 | 19 Pages |
Abstract
In this paper we describe a segmentation method applied to images which are compressed by using Fuzzy Transforms. The segmentation of the images is realized via the FGFCM (Fast Generalized Fuzzy C-Means) clustering algorithm, which is robust to noise and outliers. The optimal number of clusters is determined via the PCAES (Partition Coefficient And Exponential Separation) validity index. We use a similarity measure defined via Lukasiewicz t-norm for comparison between the original image and the reconstructed images. The best results are obtained if this similarity measure overcomes a threshold value, experimentally determined from the analysis of the trend of it with respect to the PSNR (Peak Signal to Noise Ratio).
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence