Abstract
Matching within an inspection system is one of the most crucial processing steps in testing how well measures, shape or completeness of an industrial machined work-piece fits the important properties of its “golden prototype”.
Especially, pure image based systems suffer from the fact that pixel-to-pixel comparisons are effected by variations in scale, illumination, positioning accuracy or tolerances of single properties of the specimen. Additionally, unprecisely defined features of the test patterns, incomplete specifications of all details to be tested, restrict automation of visual inspection still to few applications only. Knowledge-based matching using a priori information about specimen (e.g. CAD data) and inspection environment would overcome these disadvantages.
This paper presents an overview about matching algorithms in knowledge-based visual inspection. From this survey a list of assessment criteria for matching schemes is derived. Initial ideas for a generic matching concept using graph matching techniques is proposed. Discussion of the new approach is mainly devoted to control of matching by prediction graphs derived from model data.
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.
References
D.H.Ballard, C.M.Brown: Computer Vision, Prentice Hall Inc., 1982.
A.T.Berztiss: A Backtrack Procedure for Isomorphisms of Directed Graphs, Journal of the ACM, Vol. 17, No. 1, 51–64, 1975.
D.G.Corneil, C.C.Gotlieb: An Efficient Algorithm for Graph Isomorphism, Journal of the ACM, Vol. 17, No. 1, 51–64, 1970.
W.Frei,C.C.Chen: Fast boundary detection: a generalization and a new algorithm,IEEE Transactions on Computers, Vol.26, No.2, 988–998, 1977.
C.-E.Liedtke, M.Ender: Wissensbasierte Bildverarbeitung, Springer-Verlag, 1989.
D.G.Lowe: Three-Dimensional Object Recognition from Single Two-Dimensional Images,Artificial Intelligence, Vol.31, No.3, 355–395, 1987.
W.Menhardt: Fuzzy Relational Matching,Proceedings of the Esprit-Workshop: Advanced Matching in Vision and Artificial Intelligence, 119–133, 1990.
H.Niemann, H.Bunke: Kuenstliche Intelligenz in Bild-and Sprachanalyse, B.G.Teubner Verlag, 1987.
S.Pfleger, L.Altamirano Robles, I.Popova-Atanassova: Focus of Attention for Blur Handling, Proceedings of the Esprit-Workshop: Advanced Matching in Vision and Artificial Intelligence, 135–145, 1990.
J.R.Ullmann: An Algorithm for Sub-graph Isomorphism, Journal of the Association for Computing Machinery, Vol. 23, No. 1, 31–42, 1976.
P.H.Winston: Learning Structural Descriptions from Examples, in “The Psychology of Computer Vision”, Ed. P.H.Winston, 1975.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1991 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Carlsohn, M.F., Gruber, L. (1991). Matching: Check Points within the Framework of a Knowledge-based Visual Inspection System. In: Jordanides, T., Torby, B. (eds) Expert Systems and Robotics. NATO ASI Series, vol 71. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76465-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-76465-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-76467-7
Online ISBN: 978-3-642-76465-3
eBook Packages: Springer Book Archive