Robust pose estimation and recognition using non-gaussian modeling of appearance subspaces
We present an original appearance model that generalizes the usual Gaussian visual subspace model to non-Gaussian and nonparametric distributions. It can be useful for the modeling and recognition of images under difficult conditions such as large occlusions and cluttered backgrounds. Inference unde...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1998. - 29(2007), 5 vom: 13. Mai, Seite 901-5 |
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| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2007
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Zusammenfassung: | We present an original appearance model that generalizes the usual Gaussian visual subspace model to non-Gaussian and nonparametric distributions. It can be useful for the modeling and recognition of images under difficult conditions such as large occlusions and cluttered backgrounds. Inference under the model is efficiently solved using the mean shift algorithm |
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| Beschreibung: | Date Completed 22.05.2007 Date Revised 14.03.2007 published: Print Citation Status MEDLINE |
| ISSN: | 0162-8828 |