Robust ellipse detection with Gaussian mixture models

被引：31

作者：

Arellano, Claudia ^{[1
]}

Dahyot, Rozenn ^{[1
]}

机构：

[1] Univ Dublin Trinity Coll, Sch Comp Sci & Stat, Dublin 2, Ireland

来源：

PATTERN RECOGNITION | 2016年 / 58卷

关键词：

Ellipse detection; L2; distance; GMM; Parameter estimation; RANDOMIZED HOUGH TRANSFORM; ALGORITHM; IMAGES;

D O I：

10.1016/j.patcog.2016.01.017

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：12 / 26

页数：15

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