Abstract
We introduce a rule-based approach for the learning and recognition of complex movement sequences in terms of spatio-temporal attributes of primitive event sequences. During learning, spatio-temporal decision trees are generated that satisfy relational constraints of the training data. The resulting rules are used to classify new movement sequences, and general heuristic rules are used to combine classiffication evidences of different movement fragments. We show that this approach can successfully learn how people construct objects, and can be used to classify and diagnose unseen movement sequences.
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Bischof, W.F., Caelli, T. (2001). On the Learning of Complex Movement Sequences. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form 2001. IWVF 2001. Lecture Notes in Computer Science, vol 2059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45129-3_42
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DOI: https://doi.org/10.1007/3-540-45129-3_42
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