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dc.contributor.advisorStorkey, Amos
dc.contributor.advisorToussaint, Marc
dc.contributor.authorWilliams, Ben H
dc.date.accessioned2009-12-22T14:39:13Z
dc.date.available2009-12-22T14:39:13Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/1842/3221
dc.descriptionInstitute for Adaptive and Neural Computation
dc.description.abstractHumans and animals can plan and execute movements much more adaptably and reliably than current computers can calculate robotic limb trajectories. Over recent decades, it has been suggested that our brains use motor primitives as blocks to build up movements. In broad terms a primitive is a segment of pre-optimised movement allowing a simplified movement planning solution. This thesis explores a generative model of handwriting based upon the concept of motor primitives. Unlike most primitive extraction studies, the primitives here are time extended blocks that are superimposed with character specific offsets to create a pen trajectory. This thesis shows how handwriting can be represented using a simple fixed function superposition model, where the variation in the handwriting arises from timing variation in the onset of the functions. Furthermore, it is shown how handwriting style variations could be due to primitive function differences between individuals, and how the timing code could provide a style invariant representation of the handwriting. The spike timing representation of the pen movements provides an extremely compact code, which could resemble internal spiking neural representations in the brain. The model proposes an novel way to infer primitives in data, and the proposed formalised probabilistic model allows informative priors to be introduced providing a more accurate inference of primitive shape and timing.en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.subjectInformaticsen
dc.subjectInstitute for Adaptive and Neural Computationen
dc.subjectMarkov modelsen
dc.subjectSpike encodingen
dc.titleExtracting Motion Primitives from Natural Handwriting Dataen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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