A Bayesian Approach for Shadow Extraction from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Shadow Removal from a Single Image
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
ACM Transactions on Graphics (TOG)
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Graph cut based segmentation of soft shadows for seamless removal and augmentation
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Detecting ground shadows in outdoor consumer photographs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Distance regularized level set evolution and its application to image segmentation
IEEE Transactions on Image Processing
Shadow Removal Using Intensity Surfaces and Texture Anchor Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadow detection: A survey and comparative evaluation of recent methods
Pattern Recognition
Single-image shadow detection and removal using paired regions
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
What characterizes a shadow boundary under the sun and sky?
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Detecting soft shadows from a single image has been a difficult problem because soft shadows usually retain the textures of their backgrounds and because pixel intensities are greatly variable. In this paper, we propose a novel approach to automatically detect soft shadow edges from a single outdoor photograph. We first propose an edge-based model of a soft shadow boundary and parameterize three features. Then, motivated by the observation that the intensity profiles along the normal direction of the hard shadow edge can be approximated by a Sigmoid function, we develop an intensity model including the intensity, intensity change rate and intensity variance of the soft shadow. By fitting the intensity model of the soft shadows, three features of the boundary model are obtained and the initial segments of the soft shadow boundary are localized. Finally, to generate a continuous boundary, a level set-based optimization process is utilized with a global constraint on shadow width. Experiments demonstrate the effectiveness and flexibility of our method.