Calibrating an automatic zoom camera with moving least squares

Michel Sarkis, Christian T. Senft, Klaus Diepold

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


The application of zoom camera lenses in machine vision has gained a lot of attention lately. The main difficulty in their employment lies in the accurate estimation of their intrinsic parameters. In this paper, we propose novel approaches to determine these parameters by estimating continuous models of their variations as the focus and the zoom change. The first method is based on the moving least squares (MLS) multiple regression scheme which determines from a predefined number of samples, the complete model of the intrinsic parameters. MLS fits a polynomial function at each focus and zoom setting by using the measured neighboring points. In order to reduce the computational complexity of MLS, we propose another algorithm in which the MLS generated curves are clustered. Then, each cluster is modeled with a single polynomial function. This decreases the complexity of computations for the applications where delay is critical, e.g., telepresence, to the evaluation of simple polynomials. Compared to previous techniques, the proposed algorithms lead to a noticeable increase in the estimation accuracy of the intrinsic parameters. In addition, they are able to generate accurate models of these parameters with only a few measurement points.

Original languageEnglish
Article number4982666
Pages (from-to)492-503
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Issue number3
StatePublished - Jul 2009


  • Interpolation
  • Least squares methods
  • Lenses
  • Machine vision
  • Modeling


Dive into the research topics of 'Calibrating an automatic zoom camera with moving least squares'. Together they form a unique fingerprint.

Cite this