Recognising Dutch Sign Language Signs Using the Kinect: Hidden Markov Models vs Dynamic Time Warping

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dc.contributor.advisorVuurpijl, L.G.
dc.contributor.advisorGrzyb, B.J.
dc.contributor.authorThiel, B. van
dc.date.issued2017-06-29
dc.description.abstractThis paper compares the abilities of Hidden Markov Models and Dynamic Time Warping to classify Dutch Sign Language gestures with few examples. As communication with computers becomes more important researchers need to know the methods they should use to maximise their results given their data. 9 subjects recorded gestures for this experiment. With enough examples Hidden Markov Models can give you a good classification, but with few examples Dynamic Time Warping proves to be a better classifier.en_US
dc.embargo.lift3000-12-31
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/5278
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleRecognising Dutch Sign Language Signs Using the Kinect: Hidden Markov Models vs Dynamic Time Warpingen_US
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