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Processing Sparse Panoramic Images via Space Variant Operators

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Abstract

The use of omni-directional cameras has become increasingly popular in vision systems for video surveillance and autonomous robot navigation. However, to date most of the research relating to omni-directional cameras has focussed on the design of the camera or the way in which to project the omni-directional image to a panoramic view rather than the processing of such images after capture. Typically images obtained from omni-directional cameras are transformed to sparse panoramic images that are interpolated to obtain a complete panoramic view prior to low level image processing. This interpolation presents a significant computational overhead with respect to real-time vision.

We present an efficient design procedure for space variant feature extraction operators that can be applied to a sparse panoramic image and directly processes this sparse image. This paper highlights the reduction of the computational overheads of directly processing images arising from omni-directional cameras through efficient coding and storage, whilst retaining accuracy sufficient for application to real-time robot vision.

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Authors and Affiliations

  1. School of Computing and Intelligent Systems, University of Ulster, Northland Road, Londonderry, BT48 7JL, Northern Ireland

    Sonya Coleman & Dermot Kerr

  2. School of Computing and Information Engineering, University of Ulster, Cromore Road, Coleraine, BT52 1SA, Northern Ireland

    Bryan Scotney

Authors
  1. Sonya Coleman
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  2. Bryan Scotney
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  3. Dermot Kerr
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Correspondence to Sonya Coleman.

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Coleman, S., Scotney, B. & Kerr, D. Processing Sparse Panoramic Images via Space Variant Operators. J Math Imaging Vis 32, 349–361 (2008). https://doi.org/10.1007/s10851-008-0107-0

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  • DOI: https://doi.org/10.1007/s10851-008-0107-0

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