IEEE Trans Image Process - A new scheme for robust gradient vector estimation in color images.

Tópicos

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Resumo

Gradient estimators are mostly designed to yield accurate and robust estimates of the gradient magnitude, not the gradient direction. This paper proposes a method for the accurate and robust estimation of both the gradient magnitude and direction. It robustly estimates the gradient in the x- and y-directions. The robustness against noise is achieved by prefiltering and postfiltering of the gradient in each direction. To reduce edge blurring effects introduced by these filters, the gradient in a certain direction is obtained by applying the prefilter and postfilter in the perpendicular direction. The basic elements employed in each window are: highpass, lowpass and aggregation operators. The highpass operator is used as a gradient estimator, the lowpass operator is for prefiltering and postfiltering, and the aggregation operator is for aggregating the prefiltered and postfiltered gradients. Four different combinations of highpass, lowpass and aggregation operators are proposed: MVD-Median-Mean, MVD-Median-Max, RCMG-Median-Mean, and RCMG-Median-Max. Experimental results show that the RCMG-Median-Mean has the best performance in estimating the gradient and detecting the edges in noisy color images. It is computationally more efficient than the state-of-the-art gradient estimators and is able to accurately estimate the gradient direction as well as the gradient magnitude. Computer simulation results show that the proposed method outperforms other recently proposed color gradient estimators and edge detectors.

Resumo Limpo

gradient estim most design yield accur robust estim gradient magnitud gradient direct paper propos method accur robust estim gradient magnitud direct robust estim gradient x ydirect robust nois achiev prefilt postfilt gradient direct reduc edg blur effect introduc filter gradient certain direct obtain appli prefilt postfilt perpendicular direct basic element employ window highpass lowpass aggreg oper highpass oper use gradient estim lowpass oper prefilt postfilt aggreg oper aggreg prefilt postfilt gradient four differ combin highpass lowpass aggreg oper propos mvdmedianmean mvdmedianmax rcmgmedianmean rcmgmedianmax experiment result show rcmgmedianmean best perform estim gradient detect edg noisi color imag comput effici stateoftheart gradient estim abl accur estim gradient direct well gradient magnitud comput simul result show propos method outperform recent propos color gradient estim edg detector

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