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Content Provider | IEEE Xplore Digital Library |
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Author | Xian Xiao Changsheng Xu Yong Rui |
Copyright Year | 2010 |
Description | Author affiliation: Microsoft China R&D Group, Beijing, 100080, China (Yong Rui) || National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing 100190, China (Xian Xiao; Changsheng Xu) |
Abstract | 3D reconstruction has been widely used in many important applications. While extensive research has been done in 3D reconstruction, several key issues are still open and the precision of the recovered regions is still far from satisfaction. In this paper, we propose a novel approach to selecting regions of interest in video frames by analyzing multiple spatio-temporal characteristics and reconstructing 3D objects based on the selected regions. Firstly, the static, location and motion attention are extracted from video frames to generate saliency maps. Then, all the video frames are clustered and a candidate set of key frames is extracted based on the saliency maps, where the key frames are extracted according to the constraints in terms of geometry and visibility. Finally, the 3D structure of the attention region is recovered using the selected key frames and the generated saliency maps. The experiments on real-world indoor and outdoor scenes demonstrate that the proposed approach is both more accurate (better attention regions) and computationally more efficient. |
Starting Page | 1091 |
Ending Page | 1096 |
File Size | 1175263 |
Page Count | 6 |
File Format | |
ISBN | 9781424474912 |
ISSN | 1945788X |
e-ISBN | 9781424474936 |
DOI | 10.1109/ICME.2010.5582982 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-07-19 |
Publisher Place | Singapore |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Three dimensional displays Visualization Cameras Humans Feature extraction Image reconstruction Computer vision 3D reconstruction Visual attention Video analysis Key frames selection |
Content Type | Text |
Resource Type | Article |
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