IEEE Trans Image Process - Image registration under illumination variations using region-based confidence weighted M-estimators.

Tópicos

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{ model(2656) set(1616) predict(1553) }
{ imag(1057) registr(996) error(939) }
{ method(2212) result(1239) propos(1039) }
{ estim(2440) model(1874) function(577) }
{ problem(2511) optim(1539) algorithm(950) }
{ control(1307) perform(991) simul(935) }
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{ general(901) number(790) one(736) }
{ cost(1906) reduc(1198) effect(832) }
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Resumo

We present an image registration model for image sets with arbitrarily shaped local illumination variations between images. Any nongeometric variations tend to degrade the geometric registration precision and impact subsequent processing. Traditional image registration approaches do not typically account for changes and movement of light sources, which result in interimage illumination differences with arbitrary shape. In addition, these approaches typically use a least-square estimator that is sensitive to outliers, where interimage illumination variations are often large enough to act as outliers. In this paper, we propose an image registration approach that compensates for arbitrarily shaped interimage illumination variations, which are processed using robust M -estimators tuned to that region. Each M-estimator for each illumination region has a distinct cost function by which small and large interimage residuals are unevenly penalized. Since the segmentation of the interimage illumination variations may not be perfect, a segmentation confidence weighting is also imposed to reduce the negative effect of mis-segmentation around illumination region boundaries. The proposed approach is cast in an iterative coarse-to-fine framework, which allows a convergence rate similar to competing intensity-based image registration approaches. The overall proposed approach is presented in a general framework, but experimental results use the bisquare M-estimator with region segmentation confidence weighting. A nearly tenfold improvement in subpixel registration precision is seen with the proposed technique when convergence is attained, as compared with competing techniques using both simulated and real data sets with interimage illumination variations.

Resumo Limpo

present imag registr model imag set arbitrarili shape local illumin variat imag nongeometr variat tend degrad geometr registr precis impact subsequ process tradit imag registr approach typic account chang movement light sourc result interimag illumin differ arbitrari shape addit approach typic use leastsquar estim sensit outlier interimag illumin variat often larg enough act outlier paper propos imag registr approach compens arbitrarili shape interimag illumin variat process use robust m estim tune region mestim illumin region distinct cost function small larg interimag residu uneven penal sinc segment interimag illumin variat may perfect segment confid weight also impos reduc negat effect missegment around illumin region boundari propos approach cast iter coarsetofin framework allow converg rate similar compet intensitybas imag registr approach overal propos approach present general framework experiment result use bisquar mestim region segment confid weight near tenfold improv subpixel registr precis seen propos techniqu converg attain compar compet techniqu use simul real data set interimag illumin variat

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