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Home Members Julia Diebold

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: und in ihrem Blog


List of publications.

Book Chapters
Skeleton-Based Recognition of Shapes in Images via Longest Path Matching (G. Bal, J. Diebold, E. W. Chambers, E. Gasparovic, R. Hu, K. Leonard, M. Shaker, C. Wenk), Chapter in Research in Shape Modeling, Springer International Publishing, volume 1, 2015. [bib] [pdf] [doi]
Journal Articles
The Role of Diffusion in Figure Hunt Games (J. Diebold, S. Tari, D. Cremers), In Journal of Mathematical Imaging and Vision, Springer, volume 52, 2015. [bib] [pdf] [doi]
Midrange Geometric Interactions for Semantic Segmentation (J. Diebold, C. Nieuwenhuis, D. Cremers), In International Journal of Computer Vision, Springer US, 2015. [bib] [pdf] [doi]
Conference and Workshop Papers
Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation (C. Hazirbas, J. Diebold, D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code]) [bib] [pdf] [doi]Oral Presentation
Interactive Multi-label Segmentation of RGB-D Images (J. Diebold, N. Demmel, C. Hazirbas, M. Möller, D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code]) [bib] [pdf] [doi]
Learning Nonlinear Spectral Filters for Color Image Reconstruction (M. Moeller, J. Diebold, G. Gilboa, D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2015. [bib] [pdf]
Flow and Color Inpainting for Video Completion (M. Strobel, J. Diebold, D. Cremers), In German Conference on Pattern Recognition (GCPR), 2014. [bib] [pdf] [doi]Oral Presentation
Proximity Priors for Variational Semantic Segmentation and Recognition (J. Bergbauer, C. Nieuwenhuis, M. Souiai, D. Cremers), In ICCV Workshop on Graphical Models for Scene Understanding, 2013. [bib] [pdf] [doi]
Top-down visual search in Wimmelbild (Julia Bergbauer, Sibel Tari), In Proceedings of SPIE, Human Vision and Electronic Imaging XVIII, 2013. [bib] [pdf] [doi]
Wimmelbild Analysis with Approximate Curvature Coding Distance Images (Julia Bergbauer, Sibel Tari), In Scale Space and Variational Methods in Computer Vision (A. Kuijper, K. Bredies, T. Pock, H. Bischof, eds.), Springer, volume 7893, 2013. [bib] [pdf] [doi]Oral Presentation
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Last edited 19.01.2016 09:55 by dieboldj