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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jay, G.T. Smith, R. |
| Copyright Year | 2008 |
| Description | Author affiliation: Univ. of Alabama, Tuscaloosa, AL (Jay, G.T.; Smith, R.) |
| Abstract | Structured light systems use projected light to augment a scene with extra information. The goal of such systems is often the recovery of depth information based on 2D image(s) from one or more viewpoints. Traditionally, the best performing systems use the injected information to ldquolabelrdquo points within a scene. These labelings provide the correspondence information needed for 3D reconstruction. In this paper we attempt to demonstrate the feasibility of an alternative, similarity metric based, techinque by presenting a simple proof-of-concept system. The technique consists of performing a search for corresponding points from two image sets via a similarity metric and then using the best correspondences as an approximation of the actual point correspondences. Remarkably, we find that this conceptually simple approach can generate data of comparable quality to more complicated techniques. Perhaps the most striking advantage to this approach is that it eliminates the need for camera calibration. We believe these initial results indicate great promise for the application of ldquodata-miningrdquo techniques to the 3D acquisition domain. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 448559 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424421749 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2008.4761305 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-08 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Layout Cameras Labeling Image reconstruction Calibration Prototypes Clouds Optical surface waves Heart Engines |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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