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Noise space decomposition method for two-dimensional sinusoidal model. (English) Zbl 1365.62363

Summary: The estimation of the parameters of the two-dimensional sinusoidal signal model has been addressed. The proposed method is the two-dimensional extension of the one-dimensional noise space decomposition method. It provides consistent estimators of the unknown parameters and they are non-iterative in nature. Two pairing algorithms, which help in identifying the frequency pairs have been proposed. It is observed that the mean squared errors of the proposed estimators are quite close to the asymptotic variance of the least squares estimators. For illustrative purposes two data sets have been analyzed, and it is observed that the proposed model and the method work quite well for analyzing real symmetric textures.

MSC:

62M40 Random fields; image analysis
62F10 Point estimation
62F12 Asymptotic properties of parametric estimators
62M45 Neural nets and related approaches to inference from stochastic processes
60F15 Strong limit theorems
94A12 Signal theory (characterization, reconstruction, filtering, etc.)
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