A Generative Model for Separating Illumination and Reflectance from Images
Abstract
It is well known that even slight changes in nonuniform illumination lead to a large image variability and are crucial for many visual tasks. This paper presents a new ICA related probabilistic model where the number of sources exceeds the number of sensors to perform an image segmentation and illumination removal, simultaneously. We model illumination and reflectance in log space by a generalized autoregressive process and Hidden Gaussian Markov random field, respectively.
Cite
Text
Stainvas and Lowe. "A Generative Model for Separating Illumination and Reflectance from Images." Journal of Machine Learning Research, 2003.Markdown
[Stainvas and Lowe. "A Generative Model for Separating Illumination and Reflectance from Images." Journal of Machine Learning Research, 2003.](https://mlanthology.org/jmlr/2003/stainvas2003jmlr-generative/)BibTeX
@article{stainvas2003jmlr-generative,
title = {{A Generative Model for Separating Illumination and Reflectance from Images}},
author = {Stainvas, Inna and Lowe, David},
journal = {Journal of Machine Learning Research},
year = {2003},
pages = {1499-1519},
volume = {4},
url = {https://mlanthology.org/jmlr/2003/stainvas2003jmlr-generative/}
}