Abstract
Shadows often introduce errors in the performance of computer vision algorithms, such as object detection and tracking. We propose a method to remove shadows from real images based on a probability shadow map. The probability shadow map identifies how much light is impinging on a surface. The lightness of shadowed regions in an image is increased and then the color of that part of the surface is corrected so that it matches the lit part of the surface. The result is compared with two other shadow removal frameworks. The advantage of our method is that after removal, the texture and all the details in the shadowed regions remain intact.
More information:
N. Salamati, A. Germain and S. Süsstrunk, Removing Shadows from Images Using Color and Near-infrared, Proc. IEEE International Conference on Image Processing (ICIP), 2011. [detailed record] [bibtex]
References
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[3]. C. Fredembach and G. D. Finlayson, Simple shadow removal, InProc. of 18th International Conference on Pattern Recognition vol.1, pp. 137-142, 2007.