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A Space Variant Gradient Based Corner Detector for Sparse Omnidirectional Images

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Abstract

Omnidirectional cameras are useful in applications requiring rapid capture of image data representing the complete local environment. Feature detection from such image data is thus a prominent research issue. Transforming an omnidirectional image to a panoramic image may result in a sparse panoramic image with missing image data. Whilst image reconstruction techniques have been developed that enable the subsequent use of standard image processing algorithms, the development of image processing algorithms that can be applied directly to sparse image data has received less attention. We address the problem of corner point detection for sparse panoramic images by developing an algorithmic approach that can be applied directly to sparse unwarped omnidirectional images without the requirement of image reconstruction, and we illustrate the accurate performance of the algorithm through visual results and receiver operating characteristic curves.

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Authors and Affiliations

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

    Dermot Kerr & Sonya Coleman

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

    Bryan Scotney

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

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Kerr, D., Coleman, S. & Scotney, B. A Space Variant Gradient Based Corner Detector for Sparse Omnidirectional Images. J Math Imaging Vis 38, 119–131 (2010). https://doi.org/10.1007/s10851-010-0211-9

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