EM Training of Hidden Markov Models for Shape Recognition Using Cyclic Strings
![Thumbnail](/xmlui/bitstream/handle/10234/95272/61918.pdf.jpg?sequence=5&isAllowed=y)
Visualitza/
Impacte
![Google Scholar](/xmlui/themes/Mirage2/images/uji/logo_google.png)
![Microsoft Academico](/xmlui/themes/Mirage2/images/uji/logo_microsoft.png)
Metadades
Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/54899
comunitat-uji-handle4:
INVESTIGACIONMetadades
Títol
EM Training of Hidden Markov Models for Shape Recognition Using Cyclic StringsData de publicació
2013Editor
Springer Berlin HeidelbergISBN
978-3-642-42050-4; 978-3-642-42051-1ISSN
0302-9743Cita bibliogràfica
Palazón González, Vicente; Marzal Varó, Andrés; Vilar Torres, Juan Miguel. "EM Training of Hidden Markov Models for Shape Recognition Using Cyclic Strings". En: Neural Information Processing – 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part III/ Lee, M.,[et al.] (Eds.). Berlin : Springer, 2013. (Lecture Notes in Computer Science; 8228) . ISBN: 978-3-642-42050-4, pp. 317-324Tipus de document
info:eu-repo/semantics/bookPartVersió de l'editorial
http://link.springer.com/chapter/10.1007%2F978-3-642-42051-1_40Paraules clau / Matèries
Resum
Shape descriptions and the corresponding matching techniques must be robust to noise and invariant to transformations for their use in recognition tasks. Most transformations are relatively easy to handle when contours ... [+]
Shape descriptions and the corresponding matching techniques must be robust to noise and invariant to transformations for their use in recognition tasks. Most transformations are relatively easy to handle when contours are represented by strings. However, starting point invariance is difficult to achieve. One interesting possibility is the use of cyclic strings, which are strings with no starting and final points. Here we present the use of Hidden Markov Models for modelling cyclic strings and their training using Expectation Maximization. Experimental results show that our proposal outperforms other methods in the literature. [-]