Single-image shadow detection and removal using paired regions

Authors:
Ruiqi Guo; Qieyun Dai;D. Hoiem
Affiliations:
Univ. of Illinois at Urbana Champaign, Champaign, IL, USA;Univ. of Illinois at Urbana Champaign, Champaign, IL, USA;Univ. of Illinois at Urbana Champaign, Champaign, IL, USA
Venue:
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Year:
2011

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