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Segmentation and estimation of spatially varying illumination

journal contribution
posted on 2014-08-01, 00:00 authored by Lin Gu, Cong Phuoc Huynh, Antonio Robles-KellyAntonio Robles-Kelly
In this paper, we present an unsupervised method for segmenting the illuminant regions and estimating the illumination power spectrum from a single image of a scene lit by multiple light sources. Here, illuminant region segmentation is cast as a probabilistic clustering problem in the image spectral radiance space. We formulate the problem in an optimization setting, which aims to maximize the likelihood of the image radiance with respect to a mixture model while enforcing a spatial smoothness constraint on the illuminant spectrum. We initialize the sample pixel set under each illuminant via a projection of the image radiance spectra onto a low-dimensional subspace spanned by a randomly chosen subset of spectra. Subsequently, we optimize the objective function in a coordinate-ascent manner by updating the weights of the mixture components, sample pixel set under each illuminant, and illuminant posterior probabilities. We then estimate the illuminant power spectrum per pixel making use of these posterior probabilities. We compare our method with a number of alternatives for the tasks of illumination region segmentation, illumination color estimation, and color correction. Our experiments show the effectiveness of our method as applied to one hyperspectral and three trichromatic image data sets.

History

Journal

IEEE transactions on image processing

Volume

23

Issue

8

Pagination

3478 - 3489

Publisher

Institute of Electrical and Electronics Engineers

Location

Piscataway, N.J.

ISSN

1057-7149

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2014, IEEE