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Author [ Title(Desc)] Type Year
Filters: Author is Björn Ommer
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S
Ommer, B (2008). Seeing The Objects Behind The Parts: Learning Compositional Models For Visual Recognition. VDM Verlag. http://www.amazon.com/Seeing-Objects-Behind-Parts-Compositional/dp/3639021444/ref=sr_1_1?ie=UTF8&s=books&qid=1232659136&sr=1-1
C
Ommer, B and Buhmann, J M (2007). Compositional Object Recognition, Segmentation, and Tracking in Video. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 4679 318--333PDF icon Technical Report (2.78 MB)
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Ommer, B and Buhmann, J M (2007). Learning the Compositional Nature of Visual Objects. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. 1--8PDF icon Technical Report (2.78 MB)
N
Sigg, C, Fischer, B, Ommer, B, Roth, V and Buhmann, J M (2007). Nonnegative CCA for Audiovisual Source Separation. International Workshop on Machine Learning for Signal Processing. IEEE. 253--258PDF icon Technical Report (1.27 MB)
E
Roth, V and Ommer, B (2006). Exploiting Low-level Image Segmentation for Object Recognition. Pattern Recognition, Symposium of the DAGM. Springer. 4174 11--20PDF icon Technical Report (473.84 KB)
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Ommer, B and Buhmann, J M (2006). Learning Compositional Categorization Models. Proceedings of the European Conference on Computer Vision. Springer. 3953 316--329PDF icon Technical Report (1.35 MB)
Ommer, B, Sauter, M and M., B J (2006). Learning Top-Down Grouping of Compositional Hierarchies for Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Perceptual Organization in Computer Vision. IEEE. 194--194PDF icon Technical Report (358.98 KB)
O
Ommer, B and Buhmann, J M (2005). Object Categorization by Compositional Graphical Models. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 3757 235--250PDF icon Technical Report (2.07 MB)
C
Ommer, B and Buhmann, J M (2003). A Compositionality Architecture for Perceptual Feature Grouping. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 2683 275--290PDF icon Technical Report (2.89 MB)

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