Please use this identifier to cite or link to this item:
|Title:||Synthesizing oil painting surface geometry from a single photograph|
|Source:||Luo, W.,Lu, Z.,Wang, X.,Xu, Y.-Q.,Ben-Ezra, M.,Tang, X.,Brown, M.S. (2012). Synthesizing oil painting surface geometry from a single photograph. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 885-892. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2012.6247762|
|Abstract:||We present an approach to synthesize the subtle 3D relief and texture of oil painting brush strokes from a single photograph. This task is unique from traditional synthesize algorithms due to its mixed modality between the input and output; i.e., our goal is to synthesize surface normals given an intensity image input. To accomplish this task, we propose a framework that first applies intrinsic image decomposition to produce a pair of initial normal maps. These maps are combined into a conditional random field (CRF) optimization framework that incorporates additional information derived from a training set consisting of normals captured using photometric stereo on oil paintings with similar brush styles. Additional constraints are incorporated into the CRF framework to further ensures smoothness and preserve brush stroke edges. Our results show that this approach can produce compelling reliefs that are often indistinguishable from results captured using photometric stereo. © 2012 IEEE.|
|Source Title:||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 10, 2018
checked on Jan 12, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.