Robust ellipse detection with Gaussian mixture models. (October 2016) - Arellano, Claudia; Dahyot, Rozenn
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. Abstract : Highlights: We 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.