Abstract
In this paper, we present a new method for removing shadows from images. First, shadows are detected by interactive brushing assisted with a Gaussian Mixture Model. Secondly, the detected shadows are removed using an adaptive illumination transfer approach that accounts for the reflectance variation of the image texture. The contrast and noise levels of the result are then improved with a multi-scale illumination transfer technique. Finally, any visible shadow boundaries in the image can be eliminated based on our Bayesian framework. We also extend our method to video data and achieve temporally consistent shadow-free results. In this paper, we present a new method for removing shadows from images. First, shadows are detected by interactive brushing assisted with a Gaussian Mixture Model. Second, the detected shadows are removed using an adaptive illumination transfer approach that accounts for the reflectance variation of the image texture. The contrast and noise levels of the result are then improved with a multi-scale illumination transfer technique. Finally, any visible shadow boundaries in the image can be eliminated based on our Bayesian framework. We also extend our method to video data and achieve temporally consistent shadow free results.
Original language | English (US) |
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Pages (from-to) | 207-218 |
Number of pages | 12 |
Journal | Computer Graphics Forum |
Volume | 32 |
Issue number | 8 |
DOIs | |
State | Published - Jan 1 2013 |
Keywords
- gaussian mixture model
- I.3.3 [Computer Graphics]: Picture/Image Generation - Line and curve generation
- illumination transfer
- multi-scale
- shadow removal
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Graphics and Computer-Aided Design