FCM with spatial and multiresolution constraints for image segmentation

Authors:
Adel Hafiane;Bertrand Zavidovique
Affiliations:
Institut d'Electronique Fondamentale, Université de Paris-Sud, Orsay Cedex, France;Institut d'Electronique Fondamentale, Université de Paris-Sud, Orsay Cedex, France
Venue:
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Year:
2005

Citing 4
Cited 0

Data clustering: a review

ACM Computing Surveys (CSUR)
Spatial models for fuzzy clustering

Computer Vision and Image Understanding
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.01

Visualization

Abstract

A modified FCM algorithm based on spatial and multiresolution constraints is described in this paper. First the pyramid is built from the original image then in each level FCM parameters are computed under a neighborhood spatial constraint. The coarse membership functions propagate down to fine layers to improve segmentation accuracy. The algorithm is tested on both synthetic and multispectral images. Experimental results are presented, showing the effectiveness of the method.