Abstract
A systematic approach to geometrically invariant pattern description is proposed. It is based on the definition of transformation invariants. The generalized auto-correlation function is introduced as a signal representation from which such invariant descriptors can be derived. Descriptors remaining invariant under all similarity transformations are briefly discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Doyle W (1962) Operations useful for similarity-invariant pattern recognition. J ACM 9:259–267
Glünder H (1986) Neural computation of inner geometric pattern relations. Biol Cybern 55:239-151
Günder H, Kramer T (1986) Description of planar patterns by invariant features — an attempt towards the explanation of visual pattern recognition. In: Guiho G (ed) Proc of 8th ICPR. IEEE Comp Soc Press, Washington DC., pp 1090–1093
Günder H, Gerhard A, Platzer H, Hofer-Alfeis J (1984) A geometrical-transformation-invariant pattern recognition concept incorporating elementary properties of neural circuits. In: Wein M (ed) Proceedings of the 7th International Conference on Pattern Recognition (ICPR). IEEE Comp Soc Press, Washington DC., pp 1376–1379
Radig B, Schlieder C (1984) RS-automorphisms and symmetrical objects. In: Wein M (ed) Proceedings of the 7th International Conference on Pattern Recognition (ICPR). IEEE Comp Soc Press, Washington DC., pp 1138–1140
Strube HW (1985) A generalization of correlation functions and the Wiener-Khinchin theorem. Signal Process 8:63–74
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1987 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Glünder, H. (1987). Invariant Description of Pictorial Patterns via Generalized Auto-Correlation Functions. In: Meyer-Ebrecht, D. (eds) ASST ’87 6. Aachener Symposium für Signaltheorie. Informatik-Fachberichte, vol 153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-73015-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-73015-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-18401-0
Online ISBN: 978-3-642-73015-3
eBook Packages: Springer Book Archive