Researcher at Department of Information Technology, Vi3; Image Analysis
- Visiting address:
POL 104237 hus 10, Lägerhyddsvägen 1
- Postal address:
- Box 337
751 05 UPPSALA
I am a doctoral student in the Methods in Image Data Analysis (MIDA) group under the supervision of Nataša Sladoje (main supervisor), Joakim Lindblad (co-supervisor) and Ida-Maria Sintorn (co-supervisor).
My main research interests are in efficient fusion of intensity and spatial information, distance/similarity measures between sets/images, image registration, robust and general methods, and machine learning methods.
Keywords: image analysis machine learning deep learning optimization robustness similarity measures image registration distance transforms
L Solorzano, L. Wik, T. O. Bontell, Y. Wang, A. H. Klemm, J. Öfverstedt, A. S. Jakola, A. Östman, C. Wählby: Machine learning for cell classification and neighborhood analysis in glioma tissue. Cytometry Part A, 2021.
N. Pielawski, E. Wetzer, J. Öfverstedt, J. Lu, C. Wählby, J. Lindblad, and N. Sladoje. CoMIR: Contrastive Multimodal Image Representations for Registration. NeurIPS 2020.
J. Öfverstedt, J. Lindblad, and N. Sladoje. Stochastic Distance Transform: Theory, Algorithms, and Applications. Journal of Mathematical Imaging and Vision, 62(5), 751-769, 2020. (Online - Open Access/CC BY)
J. Öfverstedt, J. Lindblad, and N. Sladoje. Stochastic Distance Transform. (Preprint - arXiv:1810.08097 [cs.CV]). In Proceedings of the 21th international conference on Discrete Geometry for Computer Imagery (DGCI), Lecture Notes in Computer Science, LNCS-11134, pp. 75--86, Paris, France, March 2019. (Online).
J. Öfverstedt, J. Lindblad, and N. Sladoje. Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information. IEEE Transactions on Image Processing, Vol. 27, No. 7, pp. 3584-3597, 2019. (Online - Open Access/CC BY) (Preprint - arXiv:1807.11599 [cs.CV]).
J. Öfverstedt, N. Sladoje, and J. Lindblad. Distance Between Vector-valued Fuzzy Sets based on Intersection Decomposition with Applications in Object Detection. In Proc. of the 13th International Symposium on Mathematical Morphology, ISMM2017, Fontainebleau, France, Lecture Notes in Computer Science, LNCS-10225, pp. 395-407, Springer 2017.
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