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Content Provider | IEEE Xplore Digital Library |
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Author | Wei Luo Zheng Lu Xiaogang Wang Ying-Qing Xu Ben-Ezra, M. Xiaoou Tang Brown, M.S. |
Copyright Year | 2012 |
Description | Author affiliation: National University of Singapore (Zheng Lu; Brown, M.S.) || Microsoft Research Asia (Ben-Ezra, M.) || Tsinghua University (Ying-Qing Xu) || The Chinese University of Hong Kong (Wei Luo; Xiaogang Wang; Xiaoou Tang) |
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. |
Starting Page | 885 |
Ending Page | 892 |
File Size | 922102 |
Page Count | 8 |
File Format | |
ISBN | 9781467312264 |
ISSN | 10636919 |
e-ISBN | 9781467312288 |
e-ISBN | 9781467312271 |
DOI | 10.1109/CVPR.2012.6247762 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-06-16 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Painting Lighting Training data Surface reconstruction Training Image color analysis |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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