Martin Schiegg
Martin Schiegg
Research Scientist, Bosch Center for AI
Bestätigte E-Mail-Adresse bei bosch.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Graphical model for joint segmentation and tracking of multiple dividing cells
M Schiegg, P Hanslovsky, C Haubold, U Koethe, L Hufnagel, ...
Bioinformatics 31 (6), 948-956, 2015
702015
Conservation tracking
M Schiegg, P Hanslovsky, BX Kausler, L Hufnagel, FA Hamprecht
Proceedings of the IEEE International Conference on Computer Vision, 2928-2935, 2013
592013
A discrete chain graph model for 3d+ t cell tracking with high misdetection robustness
BX Kausler, M Schiegg, B Andres, M Lindner, U Koethe, H Leitte, ...
European Conference on Computer Vision, 144-157, 2012
472012
Ilastik: interactive machine learning for (bio) image analysis
S Berg, D Kutra, T Kroeger, CN Straehle, BX Kausler, C Haubold, ...
Nature Methods, 1-7, 2019
382019
Probabilistic recurrent state-space models
A Doerr, C Daniel, M Schiegg, D Nguyen-Tuong, S Schaal, M Toussaint, ...
arXiv preprint arXiv:1801.10395, 2018
312018
Segmenting and Tracking Multiple Dividing Targets Using ilastik
C Haubold, M Schiegg, A Kreshuk, S Berg, U Koethe, FA Hamprecht
Focus on bio-image informatics, 199-229, 2016
282016
Tracking indistinguishable translucent objects over time using weakly supervised structured learning
L Fiaschi, F Diego, K Gregor, M Schiegg, U Koethe, M Zlatic, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014
232014
Active structured learning for cell tracking: algorithm, framework, and usability
X Lou, M Schiegg, FA Hamprecht
IEEE Transactions on Medical Imaging 33 (4), 849-860, 2014
192014
Time series anomaly detection based on shapelet learning
L Beggel, BX Kausler, M Schiegg, M Pfeiffer, B Bischl
Computational Statistics 34 (3), 945-976, 2019
92019
Markov logic mixtures of Gaussian processes: Towards machines reading regression data
M Schiegg, M Neumann, K Kersting
Artificial Intelligence and Statistics, 1002-1011, 2012
92012
Proof-reading guidance in cell tracking by sampling from tracking-by-assignment models
M Schiegg, B Heuer, C Haubold, S Wolf, U Koethe, FA Hamprecht
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 394-398, 2015
32015
Relational generalized few-shot learning
X Shi, L Salewski, M Schiegg, Z Akata, M Welling
arXiv preprint arXiv:1907.09557, 2019
22019
Differentiable Likelihoods for Fast Inversion of'Likelihood-Free'Dynamical Systems
H Kersting, N Krämer, M Schiegg, C Daniel, M Tiemann, P Hennig
arXiv preprint arXiv:2002.09301, 2020
12020
Learning diverse models: The coulomb structured support vector machine
M Schiegg, F Diego, FA Hamprecht
European Conference on Computer Vision, 585-599, 2016
12016
Multi-target tracking with probabilistic graphical models
MJ Schiegg
12015
Model calculating unit and control unit for selectively calculating an rbf model, a gaussian process model and an mlp model
A Guntoro, E Kloppenburg, H Markert, M Schiegg
US Patent App. 16/466,731, 2019
2019
Method, device and computer program for ascertaining an anomaly
B Kausler, L Beggel, M Schiegg, M Pfeiffer
US Patent App. 16/183,807, 2019
2019
Markov Logic Mixtures of Gaussian Processes: Combining Probabilistic Regression and Relational Knowledge Bases
M Schiegg
Verlag nicht ermittelbar, 2011
2011
HD-Hau-GE
C Haubold, M Schiegg, D Stöckel, S Wolf, FA Hamprecht
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