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)


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.


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.


  1. 1.
    Becker, E.B., Carey, G.F., Oden, J.T.: Finite Elements: An Introduction. Prentice Hall, London (1981)zbMATHGoogle Scholar
  2. 2.
    Petrou, M., Piroddi, R., Chandra, S.: Irregularly Sampled Scenes. In: Proceedings of SPIE Image and Signal Processing for Remote Sensing, vol. SPIE5573 (2004)Google Scholar
  3. 3.
    Piroddi, R., Petrou, M.: Dealing with Irregular Samples. Advances in Imaging and Electron Physics 132, 109–165 (2004)CrossRefGoogle Scholar
  4. 4.
    Ramponi, G., Carrato, S.: An Adaptive Irregular Sampling Algorithm and its Application to Image Coding. Image and Vision Computing 19, 451–460 (2001)CrossRefGoogle Scholar
  5. 5.
    Scotney, B.W., Coleman, S.A., Herron, M.G.: A Systematic Design Procedure for Scalable Near-Circular Gaussian Operators. In: Proc. IEEE ICIP, pp. 844–847 (2001)Google Scholar
  6. 6.
    Scotney, B.W., Coleman, S.A., Herron, M.G.: Device Space Design for Efficient Scale-Space Edge Detection. In: Sloot, P.M.A., Tan, C.J.K., Dongarra, J., Hoekstra, A.G. (eds.) ICCS-ComputSci 2002. LNCS, vol. 2329, pp. 1077–1086. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Shewchuk, J.R.: Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator. In: 1st Workshop on Applied Computational Geometry, pp. 124–133 (1996)Google Scholar
  8. 8.
    Stasinski, R., Konrad, J.: POCS-Based Image Reconstruction from Irregularly-Spaced Samples. In: Proceedings of IEEE International Conference on Image Processing, pp. 315–318 (2000)Google Scholar
  9. 9.
    Vazquz, C., Dubois, E., Konrad, J.: Reconstruction of Irregularly-Sampled Images by Regularization in Spline Spaces. In: Proceedings of IEEE International Conference on Image Processing, pp. 405–408 (2002)Google Scholar
  10. 10.
    Vazquz, C., Konrad, J., Dubois, E.: Wavelet-Based Reconstruction of Irregularly-Sampled Images: Application to Stereo Imaging. In: Proceedings of IEEE International Conference on Image Processing, pp. 319–322 (2000)Google Scholar
  11. 11.
    Yegnanarayana, B., Mariadassou, C.P., Saini, P.: Signal Reconstruction from Partial Data for Sensor Array Imaging applications. Signal Processing 19, 139–149 (1990)CrossRefMathSciNetGoogle Scholar

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

Personalised recommendations