Multi-modal registration using a combined similarity measure

Juan Wachs, Helman Stern, Tom Burks, Victor Alchanatis

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus citations


In this paper we compare similarity measures used for multi-modal registration, and suggest an approach that combines those measures in a way that the registration parameters are weighted according to the strength of each measure. The measures used are: (1) cross correlation normalized, (2) correlation coefficient, (3) correlation coefficient normalized, (4) the Bhattacharyya coefficient, and (5) the mutual information index. The approach is tested on fruit tree registration using multiple sensors (RGB and infra-red). The combination method finds the optimal transformation parameters for each new pair of images to be registered. The method uses a convex linear combination of weighted similarity measures in its objective function. In the future, we plan to use this methodology for an on-tree fruit recognition system in the scope of robotic fruit picking.

Original languageEnglish
Title of host publicationApplications of Soft Computing
Subtitle of host publicationUpdating the State of Art
EditorsErel Avineri, Yos Sunitiyoso, Mario Koppen, Keshav Dahal, Rajkumar Roy
Number of pages10
StatePublished - 11 Feb 2009

Publication series

NameAdvances in Soft Computing
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794


  • Multi-modal registration
  • Mutual information
  • Sensor fusion
  • Similarity measures

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mechanics
  • Computer Science Applications


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