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Autonomous Operators for Direct Use on Irregular Image Data

  • S. A. Coleman
  • B. W. Scotney
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

Standard image processing algorithms for digital images require the availability of complete, and regularly sampled, image data. This means that irregular image data must undergo reconstruction to yield regular images to which the algorithms are then applied. The more successful image reconstruction techniques tend to be expensive to implement. Other simpler techniques, such as image interpolation, whilst cheaper, are usually not adequate to support subsequent reliable image processing. This paper presents a family of autonomous image processing operators constructed using the finite element framework that enable direct processing of irregular image data without the need for image reconstruction. The successful use of reduced data (as little as 10% of the original image) affords rapid, accurate, reliable, and computationally inexpensive image processing techniques.

Keywords

Image Interpolation Autonomous Operator Irregular Mesh Gaussian Basis Function Finite Element Framework 
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 2005

Authors and Affiliations

  • S. A. Coleman
    • 1
  • B. W. Scotney
    • 2
  1. 1.School of Computing and Intelligent SystemsUniversity of UlsterLondonderryNorthern Ireland
  2. 2.School of Computing and Information EngineeringUniversity of UlsterColeraineNorthern Ireland

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