A method to add Hidden Markov Models with application to learning articulated motion

Hicks, Yulia Alexandrovna ORCID: https://orcid.org/0000-0002-7179-4587, Hall, Peter M. and Marshall, Andrew David ORCID: https://orcid.org/0000-0003-2789-1395 2003. A method to add Hidden Markov Models with application to learning articulated motion. Presented at: British Machine Vision Conference, Norwich, England, 8-11 Sept 2003.

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Abstract

In this paper we present a method for adding Hidden Markov Models. The main advantages of our method are that it does not require the data the models had been trained on, allows a change in the number of components, does not assume independence of the components to be added and is resistant to the order in which the training data arrives. We assessed the method in the experiments with synthetic data, which showed good accuracy. Finally, we present an application in computer vision.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Uncontrolled Keywords:	Markov models ; Computer vision
Last Modified:	17 Oct 2022 09:40
URI:	https://orca.cardiff.ac.uk/id/eprint/5110

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