IEEE Trans Image Process - A Bayesian theoretic approach to multiscale complex-phase-order representations.

Tópicos

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Resumo

This paper explores a Bayesian theoretic approach to constructing multiscale complex-phase-order representations. We formulate the construction of complex-phase-order representations at different structural scales based on the scale-space theory. Linear and nonlinear deterministic approaches are explored, and a Bayesian theoretic approach is introduced for constructing representations in such a way that strong structure localization and noise resilience are achieved. Experiments illustrate its potential for constructing robust multiscale complex-phase-order representations with well-localized structures across all scales under high-noise situations. Illustrative examples of applications of the proposed approach is presented in the form of multimodal image registration and feature extraction.

Resumo Limpo

paper explor bayesian theoret approach construct multiscal complexphaseord represent formul construct complexphaseord represent differ structur scale base scalespac theori linear nonlinear determinist approach explor bayesian theoret approach introduc construct represent way strong structur local nois resili achiev experi illustr potenti construct robust multiscal complexphaseord represent wellloc structur across scale highnois situat illustr exampl applic propos approach present form multimod imag registr featur extract

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