PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A primitive based generative model to infer timing information in unpartitioned handwriting data
Ben Williams, Marc Toussaint and Amos Storkey
Proceedings of IJCAI 2007 2007.

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Abstract

Biological movement control and planning is based upon motor primitives. In our approach, we presume that each motor primitive takes responsibility for controlling a small sub-block of motion, containing coherent muscle activation outputs. A central timing controller cues these subroutines of movement, creating complete movement strategies that are built up by overlaying primitives, thus creating synergies of muscle activation. This partitioning allows the movement to be defined by a sparse code representing the timing of primitive activations. This paper shows that it is possible to use a factorial hidden Markov model to infer primitives in handwriting data. The variation in the handwriting data can to a large extent be explained by timing variation in the triggering of the primitives. Once an appropriate set of primitives has been inferred, the characters can be represented as a set of timings of primitive activations, along with variances, giving a very compact representation of the character. The model is naturally partitioned into a low level primitive output stage, and a top-down primitive timing stage. This partitioning gives us an insight into behaviours such as scribbling, and what is learnt in order to write a new character.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
ID Code:2430
Deposited By:Amos Storkey
Deposited On:22 November 2006

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