Robust ellipse fitting based on Lagrange programming neural network and locally competitive algorithm
Given a set of 2-dimensional (2D) scattering points, obtained from the edge detection process, the aim of ellipse fitting is to construct an elliptic equation that best fits the scattering points. However, the 2D scattering points may contain some outliers. To address this issue, we devise a robust...
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| Published in | Neurocomputing (Amsterdam) Vol. 399; pp. 399 - 413 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
25.07.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-2312 1872-8286 |
| DOI | 10.1016/j.neucom.2020.02.100 |
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| Abstract | Given a set of 2-dimensional (2D) scattering points, obtained from the edge detection process, the aim of ellipse fitting is to construct an elliptic equation that best fits the scattering points. However, the 2D scattering points may contain some outliers. To address this issue, we devise a robust ellipse fitting approach based on two analog neural network models, Lagrange programming neural network (LPNN) and locally competitive algorithm (LCA). We formulate the fitting task as a nonsmooth constrained optimization problem, in which the objective function is an approximated l0-norm term. As the LPNN model cannot handle non-differentiable functions, we utilize the internal state concept of LCA to avoid the computation of the derivative at non-differentiable points. Simulation results show that the proposed ellipse fitting approach is superior to several state-of-the-art algorithms. |
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| AbstractList | Given a set of 2-dimensional (2D) scattering points, obtained from the edge detection process, the aim of ellipse fitting is to construct an elliptic equation that best fits the scattering points. However, the 2D scattering points may contain some outliers. To address this issue, we devise a robust ellipse fitting approach based on two analog neural network models, Lagrange programming neural network (LPNN) and locally competitive algorithm (LCA). We formulate the fitting task as a nonsmooth constrained optimization problem, in which the objective function is an approximated l0-norm term. As the LPNN model cannot handle non-differentiable functions, we utilize the internal state concept of LCA to avoid the computation of the derivative at non-differentiable points. Simulation results show that the proposed ellipse fitting approach is superior to several state-of-the-art algorithms. |
| Author | Leung, Chi-Sing Constantinides, Anthony G. Liang, Junli Shi, Zhanglei So, Hing Cheung Tsang, Kim-Fung Wang, Hao |
| Author_xml | – sequence: 1 givenname: Zhanglei surname: Shi fullname: Shi, Zhanglei organization: City University of Hong Kong, Hong Kong – sequence: 2 givenname: Hao surname: Wang fullname: Wang, Hao organization: City University of Hong Kong, Hong Kong – sequence: 3 givenname: Chi-Sing surname: Leung fullname: Leung, Chi-Sing email: eeleungc@cityu.edu.hk organization: City University of Hong Kong, Hong Kong – sequence: 4 givenname: Hing Cheung surname: So fullname: So, Hing Cheung organization: City University of Hong Kong, Hong Kong – sequence: 5 givenname: Junli orcidid: 0000-0003-3306-2294 surname: Liang fullname: Liang, Junli organization: Northwestern Polytechnical University, Hong Kong – sequence: 6 givenname: Kim-Fung surname: Tsang fullname: Tsang, Kim-Fung organization: City University of Hong Kong, Hong Kong – sequence: 7 givenname: Anthony G. surname: Constantinides fullname: Constantinides, Anthony G. organization: Imperial College, Hong Kong |
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| Keywords | Outlier Locally competitive algorithm (LCA) Ellipse fitting Lagrange programming neural network (LPNN) Real-time solution |
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