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One important application of independent component analysis (ICA) is in image processing. A two dimensional (2-D) composite ICA algorithm framework for 2-D image independent component analysis (2-D ICA) is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional (2-D) image data and one-dimensional (1-D) signal. Moreover the combination of the Newton (fixed-point algorithm) and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging (fMRI) show the potential application of composite 2-D ICA in the brain activity detection.
One important application of independent component analysis (ICA) is in image processing. A two dimensional (2-D) composite ICA algorithm framework for 2-D image independent component analysis (2-D ICA) is proposed. the combination of the Newton (fixed-point algorithm) and natural gradient algorithms in this composite method increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging (fMRI) show the potential application of composite 2-D ICA in the brain activity detection.