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Content Provider | IEEE Xplore Digital Library |
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Author | Ran Song Yonghuai Liu Martin, R.R. Rosin, P.L. |
Copyright Year | 2012 |
Description | Author affiliation: Department of Computer Science and Informatics, Cardiff University, UK (Martin, R.R.; Rosin, P.L.) || Department of Computer Science, Aberystwyth University, UK (Ran Song; Yonghuai Liu) |
Abstract | We present a novel method to integrate multiple 3D scans captured from different viewpoints. Saliency information is used to guide the integration process. The multi-scale saliency of a point is specifically designed to reflect its sensitivity to registration errors. Then scans are partitioned into salient and non-salient regions through an Markov Random Field (MRF) framework where neighbourhood consistency is incorporated to increase the robustness against potential scanning errors. We then develop different schemes to discriminatively integrate points in the two regions. For the points in salient regions which are more sensitive to registration errors, we employ the Iterative Closest Point algorithm to compensate the local registration error and find the correspondences for the integration. For the points in non-salient regions which are less sensitive to registration errors, we integrate them via an efficient and effective point-shifting scheme. A comparative study shows that the proposed method delivers improved surface integration. |
Starting Page | 1474 |
Ending Page | 1481 |
File Size | 473289 |
Page Count | 8 |
File Format | |
ISBN | 9781467312264 |
ISSN | 10636919 |
e-ISBN | 9781467312288 |
e-ISBN | 9781467312271 |
DOI | 10.1109/CVPR.2012.6247836 |
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 | Vectors Robustness Iterative closest point algorithm Noise Kernel Labeling Sensitivity |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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