Material-Specific Chromaticity Priors

Jeroen Put and Nick Michiels

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.

Session

Posters 1

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DOI

10.5244/C.30.25
https://dx.doi.org/10.5244/C.30.25

Citation

Jeroen Put and Nick Michiels. Material-Specific Chromaticity Priors. In Richard C. Wilson, Edwin R. Hancock and William A. P. Smith, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 25.1-25.10. BMVA Press, September 2016.

Bibtex

        @inproceedings{BMVC2016_25,
        	title={Material-Specific Chromaticity Priors},
        	author={Jeroen Put and Nick Michiels},
        	year={2016},
        	month={September},
        	pages={25.1-25.10},
        	articleno={25},
        	numpages={10},
        	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
        	publisher={BMVA Press},
        	editor={Richard C. Wilson, Edwin R. Hancock and William A. P. Smith},
        	doi={10.5244/C.30.25},
        	isbn={1-901725-59-6},
        	url={https://dx.doi.org/10.5244/C.30.25}
        }