Spatial Models for Fuzzy Clustering
By Dzung L. Pham. Computer Vision and Image Understanding, 2001
438 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/d62490d5a7b4af39bf2a9367966dfec1b9322f82
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Shan Shen
- Malcolm H. Granat
- Annette Sterr
- William Sandham
- Arzu Ozturk
- Daniel S. Reich
- Navid Shiee
- Peter A. Calabresi
In Computational Theory and Mathematics
- Shan Shen
- Malcolm H. Granat
- Annette Sterr
- William Sandham
- Daniel S. Reich
- Navid Shiee
- Peter A. Calabresi
- Arzu Ozturk
In Computer Vision and Pattern Recognition
- Shan Shen
- Malcolm H. Granat
- Annette Sterr
- William Sandham
- Daniel S. Reich
- Peter A. Calabresi
- Navid Shiee
- Pierre-Louis Bazin
Citations in the corpus
- MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization (2005, methodology): “Optimized parameters in RFCM were obtained by means of a cross-validation method presented in [16].”
- A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions (2009, positive): “The energy function (1) is used to compute membership functions for each structure in a coordinate descent fashion, similar to the fuzzy C-means clustering algorithm and its derivative FANTASM (Fuzzy And Noise Tolerant Adaptive Segmentation Method) (Pham, 2001).”