Learning-based Shadow Recognition and Removal from Monochromatic Natural Images



Abstract:
This paper addresses the problem of recognizing and removing shadows from  monochromatic natural images.Without chromatic information, shadow classification is very challenging because the invariant color cues are unavailable. Natural scenes make this problem even harder because of ambiguity from many near black objects. We propose to use both shadow-variant and shadow-invariant cues from illumination, textural and odd order derivative characteristics. Such features are used to train a classifier from boosting a decision tree and integrated into a Conditional random Field, which can enforce local consistency over pixel labels. The proposed approach is evaluated using both qualitative and quantitative results based on a novel database of hand-labeled shadows. Based on the classification results, our system automatically remove the shadows by assuming the illumination change model in the gradient domain is approximately a Gaussian. Our results show shadowed areas of an image can be identified using proposed monochromatic cues and the recovered shadow-free images are useful for mining missing content originally hidden in shadows.
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J. Zhu, K.G.G. Samuel, S. Masood, and M.F. Tappen, Learning to Recognize Shadows in Monochromatic Natural Images. To Appear in the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2010) [PDF]


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Bibtex:

@inproceedings{Zhu_Shadows_2010,
 Author = {
J. Zhu and K. G. G. Samuel and S. Masood and M. F. Tappen},
 Title = {
Learning to Recognize Shadows in Monochromatic Natural Images},
 Booktitle = {Proceedings of the
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2010)},
 Year = {2010},
}

Color Image Database (250MB)
mat file with grayscale images and ground-truth (355 images-126MB)
mat file with grayscale images used in paper (255 images-83MB)
Color Images from single camera (128MB)

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