Adaptive-scale robust estimator using distribution model fitting

Thanh Trung Ngo, Hajime Nagahara, Ryusuke Sagawa, Yasuhiro Mukaigawa, Masahiko Yachida, Yasushi Yagi

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

1 Citation (Scopus)


We propose a new robust estimator for parameter estimation in highly noisy data with multiple structures and without prior information on the noise scale of inliers. This is a diagnostic method that uses random sampling like RANSAC, but adaptively estimates the inlier scale using a novel adaptive scale estimator. The residual distribution model of inliers is assumed known, such as a Gaussian distribution. Given a putative solution, our inlier scale estimator attempts to extract a distribution for the inliers from the distribution of all residuals. This is done by globally searching a partition of the total distribution that best fits the Gaussian distribution. Then, the density of the residuals of estimated inliers is used as the score in the objective function to evaluate the putative solution. The output of the estimator is the best solution that gives the highest score. Experiments with various simulations and real data for line fitting and fundamental matrix estimation are carried out to validate our algorithm, which performs better than several of the latest robust estimators.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages12
EditionPART 3
Publication statusPublished - 2010
Event9th Asian Conference on Computer Vision, ACCV 2009 - Xi'an, China
Duration: Sept 23 2009Sept 27 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume5996 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other9th Asian Conference on Computer Vision, ACCV 2009

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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