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Arellano, Claudia and Dahyot, Rozenn (2016) Robust ellipse detection with Gaussian mixture models. Pattern Recognition, 58. pp. 12-16. ISSN 0031-3203
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Abstract
The Euclidian distance between Gaussian Mixtures has been shown to be robust to perform point set
registration (Jian and Vemuri, 2011). We propose to extend this idea for robustly matching a family of
shapes (ellipses). Optimisation is performed with an annealing strategy, and the search for occurrences is
repeated several times to detect multiple instances of the shape of interest. We compare experimentally
our approach to other state-of-the-art techniques on a benchmark database for ellipses, and demonstrate
the good performance of our approach.
| Item Type: | Article |
|---|---|
| Keywords: | Ellipse detection; L2 distance; GMM; Parameter estimation; |
| Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 15108 |
| Identification Number: | 10.1016/j.patcog.2016.01.017 |
| Depositing User: | Rozenn Dahyot |
| Date Deposited: | 07 Dec 2021 16:35 |
| Journal or Publication Title: | Pattern Recognition |
| Publisher: | Elsevier |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/15108 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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