Fuzzy clustering algorithms with self-tuning non-local spatial information for image segmentation
Author:
- Feng ZhaoFZFeng Zhao
- 1 April 2013
- journal article
- research article
- Published by Elsevier in Neurocomputing
- Vol. 106, 115-125
Abstract
No abstract availableKeywords
This publication has 14 references indexed in Scilit:
- A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image SegmentationIEEE Transactions on Fuzzy Systems, 2008
- Image segmentation evaluation: A survey of unsupervised methodsComputer Vision and Image Understanding, 2008
- Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentationPattern Recognition, 2007
- An improved ant colony algorithm for fuzzy clustering in image segmentationNeurocomputing, 2006
- Segmentation of petrographic images by integrating edge detection and region growingComputers & Geosciences, 2004
- Efficient Graph-Based Image SegmentationInternational Journal of Computer Vision, 2004
- Robust Image Segmentation Using FCM With Spatial Constraints Based on New Kernel-Induced Distance MeasureIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004
- Suppressed fuzzy c-means clustering algorithmPattern Recognition Letters, 2003
- A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI dataIEEE Transactions on Medical Imaging, 2002
- Advances in theory and applications of fuzzy clusteringChinese Science Bulletin, 2000