IEEE Trans Pattern Anal Mach Intell - Paired Regions for Shadow Detection and Removal.

Tópicos

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Resumo

In this paper, we address the problem of shadow detection and removal from single images of natural scenes. Different from traditional methods that explore pixel or edge information, we employ a region based approach. In addition to considering individual regions separately, we predict relative illumination conditions between segmented regions from their appearances and perform pairwise classification based on such information. Classification results are used to build a graph of segments and graph-cut is used to solve the labeling of shadow and non-shadow regions. Detection results are later refined by image matting, and the shadow free image is recovered by relighting each pixel based on our lighting model. We evaluate our method on the shadow detection dataset in [1]. In addition, we created a new dataset with shadow-free ground truth images, which provides a quantitative basis for evaluating shadow removal. We study the effectiveness of features for both unary and pairwise classification.

Resumo Limpo

paper address problem shadow detect remov singl imag natur scene differ tradit method explor pixel edg inform employ region base approach addit consid individu region separ predict relat illumin condit segment region appear perform pairwis classif base inform classif result use build graph segment graphcut use solv label shadow nonshadow region detect result later refin imag mat shadow free imag recov relight pixel base light model evalu method shadow detect dataset addit creat new dataset shadowfre ground truth imag provid quantit basi evalu shadow remov studi effect featur unari pairwis classif

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