Abstract
This paper deals with the problem of automatically compiling rules which describe complex actions in terms of the spatio-temporal attributes of labeled parts. Of particular interest is the exploration of a model-based approach to induction of part attributes constrained by known properties of the generation process. The resultant algorithm is based on constraint propagation over spatiotemporal decision trees which produces Horn clause descriptions which depict the spatio-temporal properties of parts and their relations which satisfy training conditions.
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© 2001 Springer-Verlag Berlin Heidelberg
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Bischof, W.F., Caelli, T. (2001). Learning Complex Action Patterns with CRGST . In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_29
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DOI: https://doi.org/10.1007/3-540-44732-6_29
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