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Publicatie

Material-Specific Chromaticity Priors

Boekbijdrage - Boekhoofdstuk Conferentiebijdrage

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.
Boek: Proceedings of the British Machine Vision Conference 2016
Pagina's: 25.1 - 25.10
ISBN:1901725596
Jaar van publicatie:2016
Trefwoorden:intrinsic images, priors, chromaticity, materials, relighting
Toegankelijkheid:Open