Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/22872
Title: | Material-Specific Chromaticity Priors | Authors: | PUT, Jeroen MICHIELS, Nick BEKAERT, Philippe |
Issue Date: | 2016 | Publisher: | BMVA Press | Source: | Wilson, Richard C.; Hancock, Edwin R.; Smith, William A. P. (Ed.). Proceedings of the British Machine Vision Conference 2016, BMVA Press,p. 25.1-25.10 | 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. | Keywords: | intrinsic images; priors; chromaticity; materials; relighting | Document URI: | http://hdl.handle.net/1942/22872 | Link to publication/dataset: | www.bmva.org/bmvc/2016/toc.html | ISBN: | 1901725596 | Rights: | (c) 2016. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms. | Category: | C1 | Type: | Proceedings Paper |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
paper025.pdf | Published version | 2.22 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.