Camera geo-calibration using an MCMC approach

Menghua Zhai, Scott Workman, Nathan Jacobs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We address the problem of single-image geo-calibration, in which an estimate of the geographic location, viewing direction and field of view is sought for the camera that captured an image. The dominant approach to this problem is to match features of the query image, using color and texture, against a reference database of nearby ground imagery. However, this fails when such imagery is not available. We propose to overcome this limitation by matching against a geographic database that contains the locations of known objects, such as houses, roads and bodies of water. Since we are unable to find one-to-one correspondences between image locations and objects in our database, we model the problem probabilistically based on the geometric configuration of multiple such weak correspondences. We propose a Markov Chain Monte Carlo (MCMC) sampling approach to approximate the underlying probability distribution over the full geo-calibration of the camera.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
Number of pages5
ISBN (Electronic)9781467399616
StatePublished - Aug 3 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2016 IEEE.


  • Geo-calibration
  • Monte Carlo

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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