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Normalized Cross-Correlation for Spherical Images

  • Lorenzo Sorgi
  • Kostas Daniilidis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3022)

Abstract

Recent advances in vision systems have spawned a new generation of image modalities. Most of today’s robot vehicles are equipped with omnidirectional sensors which facilitate navigation as well as immersive visualization. When an omnidirectional camera with a single viewpoint is calibrated, the original image can be warped to a spherical image. In this paper, we study the problem of template matching in spherical images. The natural transformation of a pattern on the sphere is a 3D rotation and template matching is the localization of a target in any orientation. Cross-correlation on the sphere is a function of 3D-rotation and it can be computed in a space-invariant way through a 3D inverse DFT of a linear combination of spherical harmonics. However, if we intend to normalize the cross-correlation, the computation of the local image variance is a space variant operation. In this paper, we present a new cross-correlation measure that correlates the image-pattern cross-correlation with the autocorrelation of the template with respect to orientation. Experimental results on artificial as well as real data show accurate localization performance with a variety of targets.

Keywords

Spherical Harmonic Template Match Real Position Fourier Descriptor Normalize Corre 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Lorenzo Sorgi
    • 1
  • Kostas Daniilidis
    • 2
  1. 1.Italian Aerospace Research Center 
  2. 2.University of Pennsylvania 

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