Nonparametric Bayesian Line Detection: Towards Proper Priors for Robotic Computer Vision

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

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

20 Citations (Scopus)


In computer vision there are many sophisticated methods to perform inference over multiple lines, however they are quite ad-hoc. In this paper a fully Bayesian approach is used to fit multiple lines to a point cloud simultaneously. Our model extends a linear Bayesian regression model to an infinite mixture model and
uses a Dirichlet process as a prior for the partition. We perform Gibbs sampling over non-unique parameters as well as over clusters to fit lines of a fixed length, a variety of orientations, and a variable number of data points. The performance is measured using the Rand Index, the Adjusted Rand Index, and two other clustering performance indicators. This paper is mainly meant to demonstrate that general Bayesian methods can be used for line estimation. Bayesian methods, namely, given a model and noise, perform optimal inference over the data. Moreover, rather than only demonstrating the concept as such, the first results are promising with respect to the described clustering performance indicators. Further research is required to extend the method
to inference over multiple line segments and multiple volumetric objects that will need to be built on the mathematical foundation that has been laid down in this paper.
Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016
Number of pages9
ISBN (Print)978-989-758-173-1
Publication statusPublished - 2016


  • Bayesian Nonparametrics
  • Line Detection


Dive into the research topics of 'Nonparametric Bayesian Line Detection: Towards Proper Priors for Robotic Computer Vision'. Together they form a unique fingerprint.

Cite this