A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Advanced algorithms for neural networks: a C++ sourcebook
Advanced algorithms for neural networks: a C++ sourcebook
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
ICA Mixture Model based Unsupervised Classification of Hyperspectral Imagery
AIPR '02 Proceedings of the 31st Applied Image Pattern Recognition Workshop on From Color to Hyperspectral: Advancements in Spectral Imagery Exploitation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiscale Bayesian segmentation using a trainable context model
IEEE Transactions on Image Processing
Comparative analysis of fuzzy ART and ART-2A network clustering performance
IEEE Transactions on Neural Networks
A Dipolar Competitive Neural Network for Video Segmentation
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Non-local spatial spectral clustering for image segmentation
Neurocomputing
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Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein.