Intrinsic images refers to decomposition of an image into illumination S(x), reflectance (or albedo) R(x) and specular components C(x) [1].
I (x) = S(x) R(x) + C(x)
But, what are benefits of such decomposition?
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1. Shape from Shading -- Just focus on the shading effect,that is, illumination component
2. Segmentation -- separating cast shadows from image
Notes
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1. Estimating the decomposition is a ill-posed problem since for every observed pixel, we have multiple unknowns.
Reference :
[1] Grosse, Roger, et al. "Ground truth dataset and baseline evaluations for intrinsic image algorithms." ICCV 2009.
I (x) = S(x) R(x) + C(x)
But, what are benefits of such decomposition?
-----------------------------------------------
1. Shape from Shading -- Just focus on the shading effect,that is, illumination component
2. Segmentation -- separating cast shadows from image
Notes
----------
1. Estimating the decomposition is a ill-posed problem since for every observed pixel, we have multiple unknowns.
Reference :
[1] Grosse, Roger, et al. "Ground truth dataset and baseline evaluations for intrinsic image algorithms." ICCV 2009.
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