Advertisement

Detection of regions of interest via the Pyramid Discrete Symmetry Transform

  • Vito Di Gesú
  • Cesare Valenti
Part of the Advances in Computing Science book series (ACS)

Abstract

Pyramid computation has been introduced to design efficient vision algorithms [1], [2] based on both top-down and bottom-up strategies. It has been also suggested by biological arguments that show a correspondence between pyramids architecture and the mammalian visual pathway, starting from the retina and ending in the deepest layers of the visual cortex.

Keywords

Spatial Mapping Symmetry Operator Circular Symmetry Indirect Computation Medial Axis Transform 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    L.Uhr, L.: Layered Recognition Cone Networks that Preprocess, Classify and Describe. IEEE Trans.Comput., C-21, 1972.CrossRefGoogle Scholar
  2. 2.
    Pavlidis T.A., Tanimoto S.L.: A Hierarchical Data Structure for Picture Processing. Comp.Graphycs & Image Processing, Vol. 4, 1975.Google Scholar
  3. 3.
    Pomeranzt J.R. and Sager L.C.: Asymmetric integrality with dimensions of visual pattern. Perception hi Psycophysics, Vol. 18, 460–466, 1975.CrossRefGoogle Scholar
  4. 4.
    Navon D.: Forest befor trees: the precedence of global features in visual perception. Cognitive Psychology, Vol. 9, 353–385, 1977.CrossRefGoogle Scholar
  5. 5.
    Cantoni V., Di Gesii V., Ferretti M., Levialdi S., Negrini R., Stefanelli R.: The Papia System. Journal of VLSI Signal Processing, Vol. 2, 195–217, 1991.CrossRefGoogle Scholar
  6. 6.
    Kropatsch W.G., Building Irregular Pyramids by Dual Graph Contraction. Technical Report PRIP-TR-35, Institute f. Automation 183/2, Dept.for Pattern Recognition and Image Processing, TU Wien, Austria, 1995.Google Scholar
  7. 7.
    Khöler W. and Wallach H.: Figural after-effects:an investigation of visual processes. Proc. Amer. Phil. Soc., Vol. 88, 269–357, 1944.Google Scholar
  8. 8.
    Kelly M.F. and Levine M.D.: From symmetry to representation. Technical Report, TR-CIM-94–12, Center for Intelligent Machines. McGill University, Montreal, Canada, 1994.Google Scholar
  9. 9.
    Gauch J.M. and Pizer S.M.: The intensity axis of symmetry application to image segmentation. IEEE Trans. PAMI, Vol. 15, N. 8, 753–770, 1993.CrossRefGoogle Scholar
  10. [Reisfeld95]
    Reisfeld D., Wolfson H., Yeshurun Y, Context Free Attentional Operators, the Generalized Symmetry Transform. Int. Journal of Computer Vision, Vol. 14, 119130, 1995.CrossRefGoogle Scholar
  11. 10.
    Di Gesú V. and Valenti C., Symmetry Operators in Computer Vision. Vistas in Astronomy, Elsevier Science, in press.Google Scholar

Copyright information

© Springer-Verlag/Wien 1997

Authors and Affiliations

  • Vito Di Gesú
  • Cesare Valenti

There are no affiliations available

Personalised recommendations