Robust object segmentation by adaptive metrics in Generalized LVQ

Denecke A, Wersing H, Steil JJ, Körner E (2008)
In: Proc. of the European Symposium on Artificial Neural Networks (ESANN). 319-324.

Konferenzbeitrag | Veröffentlicht | Englisch
 
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Stichworte
CoR-Lab Publication
Erscheinungsjahr
2008
Titel des Konferenzbandes
Proc. of the European Symposium on Artificial Neural Networks (ESANN)
Seite(n)
319-324
Page URI
https://pub.uni-bielefeld.de/record/2142096

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Denecke A, Wersing H, Steil JJ, Körner E. Robust object segmentation by adaptive metrics in Generalized LVQ. In: Proc. of the European Symposium on Artificial Neural Networks (ESANN). 2008: 319-324.
Denecke, A., Wersing, H., Steil, J. J., & Körner, E. (2008). Robust object segmentation by adaptive metrics in Generalized LVQ. Proc. of the European Symposium on Artificial Neural Networks (ESANN), 319-324
Denecke, Alexander, Wersing, Heiko, Steil, Jochen J., and Körner, Edgar. 2008. “Robust object segmentation by adaptive metrics in Generalized LVQ”. In Proc. of the European Symposium on Artificial Neural Networks (ESANN), 319-324.
Denecke, A., Wersing, H., Steil, J. J., and Körner, E. (2008). “Robust object segmentation by adaptive metrics in Generalized LVQ” in Proc. of the European Symposium on Artificial Neural Networks (ESANN) 319-324.
Denecke, A., et al., 2008. Robust object segmentation by adaptive metrics in Generalized LVQ. In Proc. of the European Symposium on Artificial Neural Networks (ESANN). pp. 319-324.
A. Denecke, et al., “Robust object segmentation by adaptive metrics in Generalized LVQ”, Proc. of the European Symposium on Artificial Neural Networks (ESANN), 2008, pp.319-324.
Denecke, A., Wersing, H., Steil, J.J., Körner, E.: Robust object segmentation by adaptive metrics in Generalized LVQ. Proc. of the European Symposium on Artificial Neural Networks (ESANN). p. 319-324. (2008).
Denecke, Alexander, Wersing, Heiko, Steil, Jochen J., and Körner, Edgar. “Robust object segmentation by adaptive metrics in Generalized LVQ”. Proc. of the European Symposium on Artificial Neural Networks (ESANN). 2008. 319-324.
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