Shen, J. and Yang, X. and Jiang, Y. and Li, Xuelong (2011) Intrinsic images using optimization. In: UNSPECIFIED (ed.) Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers, pp. 3481-3487. ISBN 9781457703942.
Abstract
In this paper, we present a novel intrinsic image recovery approach using optimization. Our approach is based on the assumption of in a local window in natural images. Our method adopts a premise that neighboring pixels in a local window of a single image having similar intensity values should have similar reflectance values. Thus the intrinsic image decomposition is formulated by optimizing an energy function with adding a weighting constraint to the local image properties. In order to improve the intrinsic image extraction results, we specify local constrain cues by integrating the user strokes in our energy formulation, including constant-reflectance, constant-illumination and fixed-illumination brushes. Our experimental results demonstrate that our approach achieves a better recovery of intrinsic reflectance and illumination components than by previous approaches.
Metadata
| Item Type: | Book Section |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 07 Jun 2013 09:43 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7371 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
