An effective search approach to camera parameter estimation using an arbitrary planar calibration object

Zen Chen*, Chao Ming Wang, Shinn-Ying Ho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this paper we shall present a new method for camera parameter estimation using an arbitrary planar calibration object. We begin with an approximate model for the perspective view and give a parameter-free normalized-line-segment-ratio relationship between the corresponding reference and sensed views. This representation makes parameter decoupling possible and reduces the library dimension from six to one. Then the estimation problem can be viewed as a library search problem. Various mechanisms are provided to reduce the library search time, including (a) library partition by clustering, (b) sensed cluster identification by binary search, and (c) sequential testing for view matching using sorted features. In addition, feature perturbation is also considered to achieve better robustness against feature noise. Both computer-generated and real data are included in experiments, illustrating how our method works.

Original languageEnglish
Pages (from-to)655-666
Number of pages12
JournalPattern Recognition
Volume26
Issue number5
DOIs
StatePublished - 1 Jan 1993

Keywords

  • Calibration object
  • Camera parameter estimation
  • Clustering
  • Feature perturbation
  • Library partition
  • Library search
  • Ranking function

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