Name
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. HighlightsWe extend the framework based on L2 to estimate a parametric family of curves.We propose a non-isotropic and multidimensional modeling for the density functions.We propose a method for detecting multiple instances of an ellipse. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.patcog.2016.01.017 | Pattern Recognition |
Keywords | Field | DocType |
Ellipse detection,L2 distance,GMM,Parameter estimation | Point set registration,Pattern recognition,Parametric family,Euclidean distance,Gaussian,Artificial intelligence,Estimation theory,Jian,Ellipse,Machine learning,Mixture model,Mathematics | Journal |
Volume | Issue | ISSN |
58 | C | 0031-3203 |
Citations | PageRank | References |
4 | 0.38 | 24 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudia Arellano | 1 | 9 | 1.82 |
| Rozenn Dahyot | 2 | 340 | 32.62 |