Multiple Illuminant Color Estimation via Statistical Inference on Factor Graphs

This paper presents a method to recover a spatially varying illuminant color estimate from scenes lit by multiple light sources. Starting with the image formation process, we formulate the illuminant recovery problem in a statistically data-driven setting. To do this, we use a factor graph defined a...

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Bibliographic Details
Published in:IEEE transactions on image processing 2016-11, Vol.25 (11), p.5383-5396
Main Authors: Mutimbu, Lawrence, Robles-Kelly, Antonio
Format: Article
Language:English
Subjects:
colour constancy
Ensembled pixelwise illuminant estimation
Estimation
factor graphs
Image color analysis
Image segmentation
Light sources
Lighting
Prototypes
scale space
Visualization
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Summary:This paper presents a method to recover a spatially varying illuminant color estimate from scenes lit by multiple light sources. Starting with the image formation process, we formulate the illuminant recovery problem in a statistically data-driven setting. To do this, we use a factor graph defined across the scale space of the input image. In the graph, we utilize a set of illuminant prototypes computed using a data driven approach. As a result, our method delivers a pixelwise illuminant color estimate being devoid of libraries or user input. The use of a factor graph also allows for the illuminant estimates to be recovered making use of a maximum a posteriori inference process. Moreover, we compute the probability marginals by performing a Delaunay triangulation on our factor graph. We illustrate the utility of our method for pixelwise illuminant color recovery on widely available data sets and compare against a number of alternatives. We also show sample color correction results on real-world images.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2016.2605003