Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local entropy-based transition region extraction and thresholding
Pattern Recognition Letters
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
In order to effectively distinguish the objects from the background, three transition region feature models were taken into account in this paper, including gradient, local complex and local fuzzy variance. Firstly, three transition region feature models were used to distinguish the transition region and the smooth region. Secondly, experimental results signified that every feature models had effects on extracting the objects from the background and each model had some disadvantages on extracting droplets (or watermarks) from hydrophobic images. Finally, these three models were combined together to form the feature domain for Fuzzy Cmeans clustering (FCM), and then the FCM was applied in segmentation of hydrophobic images. Experimental results illustrated that the proposed algorithm has a great application in segmentation of hydrophobic images, for its efficiency in extracting shape information of droplets (or watermarks).