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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Olson, C.R. |
| Copyright Year | 2004 |
| Description | Author affiliation: Bothell Comput. & Software Syst., Washington Univ., WA, USA (Olson, C.R.) |
| Abstract | It is common in object recognition algorithms based on viewpoint consistency to find object poses that align many of the object features with features extracted from a search image. Algorithms usually treat these features as having no information other than location. However, in many applications, the features are much more distinctive than this. This distinctiveness can be used to improve recognition with respect to both the search time and the reliability of the recognition. We modify an efficient clustering method for detecting objects using geometry to incorporate short trees that help prune many of the possible matches between object features and image features prior to the more expensive clustering step. The methodology is applied to a problem of computing a spacecraft position with respect to a celestial body by recognizing the configuration of craters visible on the surface. |
| Starting Page | 149 |
| Ending Page | 152 |
| File Size | 513526 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769521282 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2004.1334083 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-08-26 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Feature extraction Pattern matching Object recognition Clustering algorithms Data mining Clustering methods Space vehicles Solid modeling Software systems Software algorithms |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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