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

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

List of publications.
Export as PDF, XML, TEX or BIB

Book Chapters
2015
[]		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 and C. Wenk), Chapter in Research in Shape Modeling, Springer International Publishing, volume 1, 2015.  [bibtex] [doi] [pdf]
Journal Articles
2016
[]		Midrange Geometric Interactions for Semantic Segmentation (J. Diebold, C. Nieuwenhuis and D. Cremers), In International Journal of Computer Vision, Springer US, volume 117, 2016. Special Issue on Graphical Models for Scene Understanding [bibtex] [pdf] [doi] [pdf]
2015
[]		The Role of Diffusion in Figure Hunt Games (J. Diebold, S. Tari and D. Cremers), In Journal of Mathematical Imaging and Vision, Springer, volume 52, 2015.  [bibtex] [doi] [pdf]
Conference and Workshop Papers
2015
[]		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] [doi] [pdf]Oral Presentation
[]		Interactive Multi-label Segmentation of RGB-D Images (J. Diebold, N. Demmel, C. Hazirbas, M. Möller and D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code]) [bibtex] [doi] [pdf]
[]		Learning Nonlinear Spectral Filters for Color Image Reconstruction (M. Moeller, J. Diebold, G. Gilboa and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2015.  [bibtex] [pdf]
2014
[]		Flow and Color Inpainting for Video Completion (M. Strobel, J. Diebold and D. Cremers), In German Conference on Pattern Recognition (GCPR), 2014.  [bibtex] [doi] [pdf]Oral Presentation
2013
[]		Proximity Priors for Variational Semantic Segmentation and Recognition (J. Bergbauer, C. Nieuwenhuis, M. Souiai and D. Cremers), In ICCV Workshop on Graphical Models for Scene Understanding, 2013.  [bibtex] [doi] [pdf]
[]		Top-down visual search in Wimmelbild (J Bergbauer and S Tari), In Proceedings of SPIE, Human Vision and Electronic Imaging XVIII, 2013.  [bibtex] [doi] [pdf]
[]		Wimmelbild Analysis with Approximate Curvature Coding Distance Images (J Bergbauer and S Tari), In Scale Space and Variational Methods in Computer Vision (A. Kuijper, K. Bredies, T. Pock, H. Bischof, eds.), Springer, volume 7893, 2013.  [bibtex] [doi] [pdf]Oral Presentation