Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Design and implementation of finite resolution crisp and fuzzy spatial objects
Data & Knowledge Engineering
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
On fuzzy cluster validity indices
Fuzzy Sets and Systems
DS '09 Proceedings of the 12th International Conference on Discovery Science
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive neuro-fuzzy system for automatic image segmentation and edge detection
IEEE Transactions on Fuzzy Systems
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In our previous work we applied fuzzy clustering to the problem of identification of upwelling areas from Sea Surface Temperature (SST) images, and showed that the approach was promising. However, the approach required a user-supplied information for annotation of the upwelling area on the map in order to fine-tune parameters of the method. In this paper, we modify the method to apply it in a fully automated manner without any pre-specified expert knowledge. We describe a computational system, FuzzyUPWELL, that provides a framework needed for a totally unsupervised segmentation and delimitation of upwelling areas on SST images. The FuzzyUPWELL system integrates an unsupervised fuzzy clustering algorithm, a threshold procedure combining a set of features extracted from clusters to determine the upwelling fronts, a mechanism to delimitate the upwelling areas by fuzzy boundaries defined from measures of classification uncertainty, and a Graphical User Interface (GUI). The system has been successfully applied to a collection of 113 images obtained at the coastal ocean of Portugal during the upwelling seasons of 1998 and 1999. The collection covers much diverse upwelling situations. The system is shown to be robust to false positives when analysing its response on SST images without upwelling.