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Blind Multi-spectral Image Decomposition by 3D Nonnegative Tensor Factorization (CROSBI ID 154372)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Kopriva, Ivica ; Cichocki, Andrzej Blind Multi-spectral Image Decomposition by 3D Nonnegative Tensor Factorization // Optics letters, 34 (2009), 14; 2210-2212

Podaci o odgovornosti

Kopriva, Ivica ; Cichocki, Andrzej

engleski

Blind Multi-spectral Image Decomposition by 3D Nonnegative Tensor Factorization

alpha-divergence based nonnegative tensor factorization (NTF) is applied to blind multi-spectral image (MSI) decomposition. Matrix of spectral profiles and matrix of spatial distributions of the materials resident in the image are identified from the factors in Tucker3 and PARAFAC models. NTF preserves local structure in the MSI that is lost, due to vectorization of the image, with nonnegative matrix factorization (NMF)- or independent component analysis (ICA)-based decompositions. Moreover, NTF based on PARAFAC model is unique up to permutation and scale under mild conditions. To achieve this, NMF- and ICA-based factorizations respectively require enforcement of sparseness (orthogonality) and statistical independence constraints on the spatial distributions of the materials resident in the MSI, and that is not true. We demonstrate efficiency of the NTF-based factorization in relation to NMF- and ICA-based factorizations on blind decomposition of the experimental MSI with the known ground truth.

Image analysis; Inverse problems; Three-dimensional image processing; Three-dimensional sensing; Medical and biological imaging

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Podaci o izdanju

34 (14)

2009.

2210-2212

objavljeno

0146-9592

Povezanost rada

Računarstvo, Temeljne medicinske znanosti, Matematika

Indeksiranost