Non-linear optimization for robust estimation of vanishing points

Nieto Doncel, Marcos and Salgado Álvarez de Sotomayor, Luis (2010). Non-linear optimization for robust estimation of vanishing points. In: "17th International Conference on Image Processing, ICIP 2010", 26/09/2010 - 29/09/2010, Hong Kong, China. ISBN 978-1-4244-7992-4.

Description

Title: Non-linear optimization for robust estimation of vanishing points
Author/s:
  • Nieto Doncel, Marcos
  • Salgado Álvarez de Sotomayor, Luis
Item Type: Presentation at Congress or Conference (Article)
Event Title: 17th International Conference on Image Processing, ICIP 2010
Event Dates: 26/09/2010 - 29/09/2010
Event Location: Hong Kong, China
Title of Book: Proceedings of IEEE 17th International Conference on Image Processing, ICIP 2010
Date: December 2010
ISBN: 978-1-4244-7992-4
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

A new method for robust estimation of vanishing points is introduced in this paper. It is based on the MSAC (M-estimator Sample and Consensus) algorithm and on the definition of a new distance function between a vanishing point and a given orientation. Apart from the robustness, our method represents a flexible and efficient solution, since it allows to work with different type of image data, and its iterative nature makes better use of the available information to obtain more accurate estimates. The key issue of the work is the proposed distance function, that makes the error to be independent from the position of an hypothesized vanishing point, which allows to work with points at the infinity. Besides, the estimation process is guided by a non-linear optimization process that enhances the accuracy of the system. The robustness of our proposal, compared with other methods in the literature is shown with a set of tests carried out for both synthetic data and real images. The results show that our approach obtain excellent levels of accuracy and that is definitely robust against the presence of large amounts of outliers, outperforming other state of the art approaches.

More information

Item ID: 9256
DC Identifier: http://oa.upm.es/9256/
OAI Identifier: oai:oa.upm.es:9256
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5652381&tag=1
Deposited by: Memoria Investigacion
Deposited on: 14 Oct 2011 07:47
Last Modified: 20 Apr 2016 17:45
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