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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22871

Title: Material-Specific Chromaticity Priors
Authors: Put, Jeroen
Michiels, Nick
Bekaert, Philippe
Issue Date: 2016
Citation: The British Machine Vision Conference (BMVC 2016), York, UK, 19-22/09/2016
Abstract: Recent advances in machine learning have enabled the recognition of high-level categories of materials with a reasonable accuracy. With these techniques, we can construct a per-pixel material labeling from a single image. We observe that groups of high-level material categories have distinct chromaticity distributions. This fact can be used to predict the range of the absolute chromaticity values of objects, provided the material is correctly labeled. We explore whether these constraints are useful in the context of the intrinsic images problem. This paper describes how to leverage material category identification to boost estimation results in state-of-the-art intrinsic images datasets.
URI: http://hdl.handle.net/1942/22871
Link to publication: www.bmva.org/bmvc/2016/papers/paper025/index.html
Category: C2
Type: Conference Material
Appears in Collections: Research publications

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