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We propose an entirely novel family of score functions for blind signal separation BSS , based on the family of mixture generalized gamma density which includes generalized gamma, Weilbull, gamma, and Laplace and Gaussian probability density functions. To blindly extract the independent source signals, we resort to the FastICA approach, whilst to adaptively(More)
An approach to multi-channel blind deconvolution is developed, which uses an adaptive filter that performs blind source separation in the Fourier space. The approach keeps (during the learning process) the same permutation and provides appropriate scaling of components for all frequency bins in the frequency space. Experiments indicate that Generalized(More)
Some of the present approaches compare the user’s query image against all of the database images; as a result, the computational complexity and search space will boost, respectively. The fundamental purpose of the research presented in the present paper is to evolve a general purpose clustering method that can efficiently and effectively handle large(More)
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