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Journal of Mathematical Imaging and Vision

, Volume 47, Issue 1–2, pp 48–59 | Cite as

A Novel Space Variant Image Representation

  • Naveen OnkarappaEmail author
  • Angel D. Sappa
Article

Abstract

Traditionally, in machine vision images are represented using cartesian coordinates with uniform sampling along the axes. On the contrary, biological vision systems represent images using polar coordinates with non-uniform sampling. For various advantages provided by space-variant representations many researchers are interested in space-variant computer vision. In this direction the current work proposes a novel and simple space variant representation of images. The proposed representation is compared with the classical log-polar mapping. The log-polar representation is motivated by biological vision having the characteristic of higher resolution at the fovea and reduced resolution at the periphery. On the contrary to the log-polar, the proposed new representation has higher resolution at the periphery and lower resolution at the fovea. Our proposal is proved to be a better representation in navigational scenarios such as driver assistance systems and robotics. The experimental results involve analysis of optical flow fields computed on both proposed and log-polar representations. Additionally, an egomotion estimation application is also shown as an illustrative example. The experimental analysis comprises results from synthetic as well as real sequences.

Keywords

Space-variant representation Log-polar mapping Onboard vision applications 

Notes

Acknowledgements

This work has been partially supported by the Spanish Government under Research Program Consolider Ingenio 2010: MIPRCV (CSD2007-00018) and Project TIN2011-25606. Naveen Onkarappa is supported by FI grant of AGAUR, Catalan Government. Authors gratefully acknowledge discussions with Henning Zimmer and Prof. Joachim Weickert. The authors would like to thank Oisin Mac Aodha for providing the Python code for raytracing with Maya, and Diego Cheda for the help in egomotion estimation.

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Computer Vision CenterBellaterra, BarcelonaSpain

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