An improved evolutionary algorithm for fundamental matrix estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The estimation of the fundamental matrix is an important problem in epipolar geometry. Many estimation methods have been proposed before, including the eight-point algorithm, Simple Evolutionary Agent (SEA) and RANSAC. In this paper, we investigate the evolutionary agent-based algorithm for fundamental matrix estimation, and present a new algorithm that improves the existing evolutionary algorithm both accuracy- and efficiency-wise. The model focuses on selecting a best combination of input points to compute the fundamental matrix via the eight-point algorithm. To improve the existing algorithm, our new model holds competition over all agents for population control and evolutionary experience accumulation. In addition to a larger competition scope, we add the outlier elimination mechanism, which greatly accelerates the algorithm. New parameters are introduced to control the convergence more efficiently. The improved algorithm achieves lower computation load and more accurate results. A general analysis about parameter selection is also provided.

Original languageEnglish (US)
Title of host publication2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
PublisherIEEE Computer Society
Pages226-231
Number of pages6
ISBN (Print)9781479907038
DOIs
StatePublished - Jan 1 2013
Event2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013 - Krakow, Poland
Duration: Aug 27 2013Aug 30 2013

Publication series

Name2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013

Other

Other2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
CountryPoland
CityKrakow
Period8/27/138/30/13

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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