Robust ellipse detection with Gaussian mixture models

Claudia Arellano, Rozenn Dahyot

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

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.

Original languageEnglish
Pages (from-to)12-26
Number of pages15
JournalPattern Recognition
Volume58
DOIs
StatePublished - 1 Oct 2016
Externally publishedYes

Keywords

  • Ellipse detection
  • GMM
  • L2 distance
  • Parameter estimation

Fingerprint

Dive into the research topics of 'Robust ellipse detection with Gaussian mixture models'. Together they form a unique fingerprint.

Cite this