Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
A customized Gabor filter for unsupervised color image segmentation
Image and Vision Computing
Color image segmentation using an enhanced Gradient Network Method
Pattern Recognition Letters
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
Non-local spatial spectral clustering for image segmentation
Neurocomputing
Colour image segmentation using fuzzy clustering techniques and competitive neural network
Applied Soft Computing
Image segmentation via coherent clustering in L*a*b* color space
Pattern Recognition Letters
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Short Communication: Multichannel image processing by using the Rank M-type L-filter
Journal of Visual Communication and Image Representation
Color image segmentation using nonparametric mixture models with multivariate orthogonal polynomials
Neural Computing and Applications - Special Issue on ICONIP2010
IEEE Transactions on Fuzzy Systems
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In this paper, we introduce two enhanced Fuzzy C-Means (FCM) clustering algorithms with spatial constraints for noisy color image segmentation. The Rank M-type L (RM-L) and L-estimators are used to obtain the sufficiently spatial information of the pixels. These estimators are involved into the FCM algorithm to provide robustness for the proposed segmentation schemes. The performance of the proposed algorithms is tested in real images under different noise conditions by simulating salt and pepper, Gaussian, and speckle noises, as well as with two mixtures of them. Simulation results indicate that the proposed methods consistently outperform other color image segmentation algorithms used as comparative. Additionally, the proposed algorithms are tested for segmenting a remote sensing image, where the noise is not known beforehand implied. Finally, the proposed algorithms have the robustness and effectiveness needed for image segmentation in the presence and absence of noise.