Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation (bibtex)
by C. Hazirbas, J. Diebold and D. Cremers
Reference:
Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation (C. Hazirbas, J. Diebold and D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code])
Bibtex Entry:
@string{ssvm="Scale Space and Variational Methods in Computer Vision (SSVM)"}
@inproceedings{hazirbas-et-al-ssvm15,
 author = {C. Hazirbas and J. Diebold and D. Cremers},
 title = {Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation},
 booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM)},
 year = {2015},
 month = {june},
 keywords = {diebold, segmentation},
 doi = {10.1007/978-3-319-18461-6_20},
 award = {Oral Presentation},
}
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Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation (bibtex)
Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation (bibtex)
by C. Hazirbas, J. Diebold and D. Cremers
Reference:
Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation (C. Hazirbas, J. Diebold and D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code])
Bibtex Entry:
@string{ssvm="Scale Space and Variational Methods in Computer Vision (SSVM)"}
@inproceedings{hazirbas-et-al-ssvm15,
 author = {C. Hazirbas and J. Diebold and D. Cremers},
 title = {Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation},
 booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM)},
 year = {2015},
 month = {june},
 keywords = {diebold, segmentation},
 doi = {10.1007/978-3-319-18461-6_20},
 award = {Oral Presentation},
}
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members:dieboldj

Research Interests

Mathematical Image Analysis, Image Segmentation, Variational Methods, Mathematical Morphology, Optimization Methods, Mathematics.

Brief Bio

Since November 2012 Julia Diebold is a PhD Student in the Research Group for Computer Vision and Pattern Recognition at the Technical University of Munich, headed by Professor Daniel Cremers.

Julia Diebold received her Bachelor of Science in Mathematics (2010) and her Master of Mathematics in Science and Engineering (2012) from the Technical University of Munich.

She received the Achievement Award for Master Graduate 2012 of the Women for Math Science Program at the Technical University of Munich.

Julia Diebold ist unter dem Namen TRYFLA als selbstständige IT-Trainerin und Beraterin in Regensburg tätig. Sie bietet IT-Kurse und Beratung rund um die Themen IT-Grundlagen, Apple, Text- und Bildbearbeitung, Internetauftritt sowie Apple Support in Regensburg an. Mehr Informationen finden Sie auf ihrer Website: http://www.tryfla.de und in ihrem Blog http://tryfla.tumblr.com

Publications

List of publications.