Abstract
This paper reports research conducted on the problem of change detection in digital imagery. The detection of changes is very important in any applications which require comparison of many images of the same scene. The problem requires an approach which is flexible and can adapt to varying data trends. The system is based on the adaptive learning networks which are an implementation of the N-tuple method of pattern recognition.
Several experiments were carried out to optimize the net parameters and test the performance of the net for this application. A new mapping structure for the N-tuple was devised to cope with insignificant scattered changes that might occuring a scene. Also the size of the minimum detectable object in a scene was also determined.
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© 1988 Springer-Verlag Berlin Heidelberg
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Asker, N.K., Hendow, N.T., Al-Muifraje, M.H. (1988). Change detection in digital imagery using the adaptive learning networks. In: Kittler, J. (eds) Pattern Recognition. PAR 1988. Lecture Notes in Computer Science, vol 301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-19036-8_11
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DOI: https://doi.org/10.1007/3-540-19036-8_11
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