Fundamentals of Nonparametric Bayesian Line Detection

Anne C. van Rossum*, Hai Xiang Lin, Johan Dubbeldam, H. Jaap van den Herik

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Line detection is a fundamental problem in the world of computer vision. Many sophisticated methods have been proposed for performing inference over multiple lines; however, they are quite ad-hoc. Our fully Bayesian model extends a linear Bayesian regression model to an infinite mixture model and uses a Dirichlet Process as a prior. Gibbs sampling over non-unique parameters as well as over clusters is performed to fit lines of a fixed length, a variety of orientations, and a variable number of data points. Bayesian inference over data is optimal given a model and noise definition. Initial computer experiments show promising results with respect to clustering performance indicators such as the Rand Index, the Adjusted Rand Index, the Mirvin metric, and the Hubert metric. In future work, this ematical foundation can be used to extend the algorithms to inference over multiple line segments and multiple volumetric objects.

Original languageEnglish
Title of host publicationPattern Recognition Applications and Methods
Subtitle of host publication5th International Conference, ICPRAM 2016, Revised Selected Papers
EditorsAna Fed, Maria De Marsico, Gabriella Sanniti di Baja
Place of PublicationCham
Number of pages19
ISBN (Electronic)978-3-319-53375-9
ISBN (Print)978-3-319-53374-2
Publication statusPublished - 2017
EventICPRAM 2016: 5th International Conference on Pattern Recognition Applications and Methods - Rome, Italy
Duration: 24 Feb 201626 Feb 2016
Conference number: 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10163 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceICPRAM 2016
Abbreviated titleICPRAM 2016
Internet address


  • Bayesian nonparametrics
  • Line detection


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