Abstract
This paper describes a probabilistic framework for recognising 2D shapes with articulated components. The shapes are represented using both geometrical and a symbolic primitives, that are encapsulated in a two layer hierarchical architecture. Each primitive is modelled so as to allow a degree of articulated freedom using a polar point distribution model that captures how the primitive movement varies over a training set. Each segment is assigned a symbolic label to distinguish its identity, and the overall shape is represented by a configuration of labels. We demonstrate how both the point-distribution model and the symbolic labels can be combined to perform recognition using a probabilistic hierarchical algorithm. This involves recovering the parameters of the point distribution model that minimise an alignment error, and recovering symbol configurations that minimise a structural error. We apply the recognition method to human pose recognition.
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© 2004 Springer-Verlag Berlin Heidelberg
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Al-Shaher, A.A., Hancock, E.R. (2004). A Probabilistic Framework for Articulated Shape Recognition. In: Perales, F.J., Draper, B.A. (eds) Articulated Motion and Deformable Objects. AMDO 2004. Lecture Notes in Computer Science, vol 3179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30074-8_8
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DOI: https://doi.org/10.1007/978-3-540-30074-8_8
Publisher Name: Springer, Berlin, Heidelberg
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