A generative model for separating illumination and reflectance from images

Inna Stainvas*, David Lowe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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. The model ability to deal with segmentation of illuminated images is compared with a Canny edge detector and homomorphic filtering. We apply the model to two problems: synthetic image segmentation and sea surface pollution detection from intensity images.

Original languageEnglish
Pages (from-to)1499-1519
Number of pages21
JournalJournal of Machine Learning Research
Issue number7-8
Publication statusPublished - Dec 2003

Bibliographical note

Copyright of the Massachusetts Institute of Technology Press (MIT Press)


  • general autoregressive model
  • iIllumination
  • Potts model
  • reflectance
  • segmentation


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