Single-image shadow detection and removal using paired regions

Ruiqi Guo, Qieyun Dai, Derek Hoiem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 [19]. In addition, we created a new dataset with shadow-free ground truth images, which provides a quantitative basis for evaluating shadow removal.

Original languageEnglish (US)
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PublisherIEEE Computer Society
Pages2033-2040
Number of pages8
ISBN (Print)9781457703942
DOIs
StatePublished - Jan 1 2011

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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