A generative model for separating illumination and reflectance from images

Stainvas, Inna and Lowe, David (2003). A generative model for separating illumination and reflectance from images. Journal of Machine Learning Research, 4 (7-8), pp. 1499-1519.

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. 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.

Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: Copyright of the Massachusetts Institute of Technology Press (MIT Press)
Uncontrolled Keywords: general autoregressive model,iIllumination,Potts model,reflectance,segmentation,Artificial Intelligence
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://jmlr.csa ... stainvas03a.pdf (Publisher URL)
Published Date: 2003-12
Authors: Stainvas, Inna
Lowe, David

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