Abstract
Shape recognition is the field of computer vision which addresses the problem of finding out whether a query shape lies or not in a shape database, up to a certain invariance. Most shape recognition methods simply sort shapes from the database along some (dis-)similarity measure to the query shape. Their main weakness is the decision stage, which should aim at giving a clear-cut answer to the question: "do these two shapes look alike?" In this article, the proposed solution consists in bounding the number of false correspondences of the query shape among the database shapes, ensuring that the obtained matches are not likely to occur "by chance". As an application, one can decide with a parameterless method whether any two digital images share some shapes or not. © 2006 Springer Science + Business Media, LLC.
| Original language | English |
|---|---|
| Pages (from-to) | 295-315 |
| Number of pages | 21 |
| Journal | International Journal of Computer Vision |
| Volume | 69 |
| Issue number | 3 |
| Early online date | 1 Apr 2006 |
| DOIs | |
| Publication status | Published - Sept 2006 |
| Externally published | Yes |
Funding
This work was supported by the Office of Naval Research under grant N00014-97-1-0839, by the Centre National d’Études Spatiales, and by the Réseau National de Recherche en Télécommunications (projet ISII). Algorithms were developed within MegaWave 2 free software. We thank the anonymous reviewers for their fruitful comments.
Keywords
- Background model
- Level lines
- Meaningful matches
- Number of false alarms
- Planar shape recognition
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