Abstract
Computer vision is an important field in industrial/automation processes. Inspection by visual means can be a powerful tool in automatic control procedures. When operating with video signals, irregularities of the optical system must often be compensated. In particular, blur, geometric distortions and the unequal brightness distribution can lead to difficulties during further processing of an image. In the following, it is shown how the theory of neural networks can be applied in image correction. The weights of one single layer are trained for calibration. Using a suitable optimisation criteria the correcting system for images superimposed by noise directly results in a Wiener Filter. A pipeline processor simulates a neural network and operates in real time. After theoretical considerations, experimental results are given in this paper.
This work was supported by a grant of the Ministry for Science and Research in Saxony-Anhalt.
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References
T. Kohonen: Self-Organisation and Associative Memory. Springer Berlin, Second Edition
P. Kanerva: Sparse Distributed Memory, Massachusetts Institute of Technology 1988: The MIT Press Cambridge, Massachusetts London, England
E. Sanchez Sinencia, C. Law: Artificial Neural Networks. IEEE Press 1992
C. Lau: Neural Networks. IEEE Press 1992
A. Cichocki, R. Unbehauen: Neural networks for optimization and signal processing. Teubner, John Wiley & Sons Ltd, 1993
W. v. Seelen: Informationsverarbeitung in homogenen Netzen von Neuronenmodellen. Dissertation Technische Hochschule Hannover 1967
H. Marko: Die Systemtheorie der homogenen Schichten. Kybernetik 5, pp. 221–240 (1969)
H. Jahn, R. Reulke: Bilddatenrestaurierung mit Subpixelgenauigkeit. 37. internationales wissenschaftliches Kolloquium Ilmenau, September 1992, Band II, pp. 515–520
F. Wahl: Digitale Bildsignalverarbeitung. Springer 1989, pp. 62–120
R. C. Gonzalez, P. Wintz: Digital Image Processing. Addison-Wesley, 1987
The digital Signal Processing Databook: Product guide INMOS. 1989
F. Wahl: Der Entwurf zweidimensionaler rekursiver Filter und ihre Anwendung in der digitalen Bildverarbeitung. Dissertation, TU München 1980
T. Sanger: Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks 2, 459–473 (1989)
NeuralWorks Professional II/PLUS and Neural Works Explorer: Reference Guide. Pittsburgh: NeuralWare Inc., 1991
Churkin, Jakowlew, Wunsch: Theorie und Anwendung der Signalabtastung. Verlag Technik Berlin 1966, pp. 90
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© 1993 Springer-Verlag Berlin Heidelberg
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Michaelis, B., Krell, G. (1993). Artificial neural networks for image improvement. In: Chetverikov, D., Kropatsch, W.G. (eds) Computer Analysis of Images and Patterns. CAIP 1993. Lecture Notes in Computer Science, vol 719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57233-3_116
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DOI: https://doi.org/10.1007/3-540-57233-3_116
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