Klassifikation von Mustern

298 LITERATURVERZEICHNIS
modeling and fast decoding in speech recognition. In Proc. European Conference on Speech
Communication and Technology, S. 687–690. Aalborg, Denmark, 2001.
[Sarkar und Chaudhuri, 1992] Sarkar, N., Chaudhuri, B.B. An efficient approach to estimate fractal
dimenison of textural images. Pattern Recognition, 25:1035–1041, 1992.
[Saupe und Vranić, 2001] Saupe, D., Vranić, D.V. 3D model retrieval with spherical harmonics and
moments. In B. Radig, S. Florczyk, Hg., Pattern Recognition. Proc. 23rd DAGM Symposium, S.
392–397. (Springer LNCS 2191, Berling Heidelberg, ISBN 3-540-42596-9), München, Germany,
2001.
[Schiele und Crowley, 1996] Schiele, B., Crowley, J.L. Object recognition using multidimensional receptive
field histograms. In B.F. Buxton, R. Cipolla, Hg., Proc. European Conference on Computer
Vision (ECCV), S. Vol. 1, 610–619. (Springer LNCS 1064, Berlin Heidelberg), Cambridge,
UK, 1996.
[Schiele und Crowley, 2000] Schiele, B., Crowley, J.L. Recognition without correspondence using multidimensional
receptive field histograms. Int. Journal of Computer Vision, 36(1):31–52, 2000.
[Schiele und Pentland, 1999] Schiele, B., Pentland, A. Probabilistic object recognition and localization.
In Proc. Int. Conference on Computer Vision (ICCV), S. Vol. 1, 177–182. (IEEE Computer Society,
Los Alamitos, California, ISBN 0-7695-0164-8), Kerkyra, Greece, 1999.
[Schless, 1999] Schless, V. Automatische Erkennung von gestörten Sprachsignalen. Dissertation, Technische
Fakultät, Universität Erlangen-Nürnberg, Erlangen, Germany, 1999.
[Schmid und Mohr, 1996] Schmid, C., Mohr, R. Combining greavalue invariants with local constraints
for object recognition. In Proc. Computer Vision and Pattern Recognition (CVPR), S. 872–877.
San Francisco, CA, USA, 1996.
[Schneeweiß, 1974] Schneeweiß. Dynamisches Programmieren. Physica Verlag, Würzburg, Wien,
1974.
[Schölkopf et al., 1999] Schölkopf, B., Mika, S., Burges, C.J.C., Knirsch, P., Müller, K.-R., Rätsch, G.,
Smola, A.J. Input space versus feature space in kernel-based methods. IEEE Trans. on Neural
Networks, 10:1000–1017, 1999.
[Schotola, 1984] Schotola, T. On the use of demisyllables in automatic word recognition. Speech Communication,
3:63–87, 1984.
[Schukat-Talamazzini, 1995] Schukat-Talamazzini, E.G. Automatische Spracherkennung. Vieweg,
Wiesbaden, 1995.
[Schulz-Mirbach, 1994] Schulz-Mirbach, H. Constructing invariant features by averaging techniques.
In Proc. Int. Conference on Pattern Recognition (ICPR), S. Vol. II, 387–390. IEEE Computer
Society Press, Jerusalem, 1994.
[Schulz-Mirbach, 1995] Schulz-Mirbach, H. Anwendung von Invarianzprinzipien zur Merkmalgewinnung
in der Mustererkennung. Fortschrittberichte, Reihe 10. VDI Verlag, Düsseldorf, Germany,
1995.
[Schwartz und Austin, 1991] Schwartz, R., Austin, S. A comparison of several approximate algorithms
for finding multiple (n-best) sentence hypotheses. In Proc. Int. Conference on Acoustics, Speech,
and Signal Processing (ICASSP), S. 701–704. Toronto, Canada, 1991.
[Seidel, 1974] Seidel, H. Verarbeitungstechniken zur Datenreduktion bei Kurzzeitfrequenzspektren von
Sprachsignalen. Dissertation, Fachbereich Elektrotechnik, Technische Universität München,
München, Germany, 1974.
[Seneff, 1986] Seneff, S. A computational model for the peripheral auditory system: Application to
speech recognition research. In Proc. Int. Conference on Acoustics, Speech, and Signal Processing
(ICASSP), S. 1983–1986. Tokyo, Japan, 1986.
[Shaick und Yaroslavski, 2001] Shaick, B.-Z., Yaroslavski, L.P. Object localization using linear adaptive
filters. In T. Ertl, B. Girod, G. Greiner, H. Niemann, H.-P. Seidel, Hg., Vision, Modeling, and
Visualization 2001 (Proceedings of the International Workshop, Stuttgart, Germany), S. 11–18.
Akademische Verlagsgesellschaft, Berlin, Germany, 2001.