Regularized mixed dimensionality and density learning in computer vision
Publication
, Journal Article
Haro, G; Randall, G; Sapiro, G
Published in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
October 11, 2007
A framework for the regularized estimation of non-uniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of Poisson mixtures, with regularizing restrictions and spatial continuity constraints. Theoretical asymptotic results for the model are presented as well, The presentation of the framework is complemented with artificial and real examples showing the importance of regularized stratification learning in computer vision applications. © 2007 IEEE.
Duke Scholars
Published In
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOI
ISSN
1063-6919
Publication Date
October 11, 2007
Citation
APA
Chicago
ICMJE
MLA
NLM
Haro, G., Randall, G., & Sapiro, G. (2007). Regularized mixed dimensionality and density learning in computer vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2007.383401
Haro, G., G. Randall, and G. Sapiro. “Regularized mixed dimensionality and density learning in computer vision.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, October 11, 2007. https://doi.org/10.1109/CVPR.2007.383401.
Haro G, Randall G, Sapiro G. Regularized mixed dimensionality and density learning in computer vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2007 Oct 11;
Haro, G., et al. “Regularized mixed dimensionality and density learning in computer vision.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Oct. 2007. Scopus, doi:10.1109/CVPR.2007.383401.
Haro G, Randall G, Sapiro G. Regularized mixed dimensionality and density learning in computer vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2007 Oct 11;
Published In
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOI
ISSN
1063-6919
Publication Date
October 11, 2007