Mikkel Fly Kragh
Mikkel Fly Kragh
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Cited by
Kinect depth sensor evaluation for computer vision applications
MR Andersen, T Jensen, P Lisouski, AK Mortensen, MK Hansen, ...
Aarhus University, 1-37, 2012
Automatic grading of human blastocysts from time-lapse imaging
MF Kragh, J Rimestad, J Berntsen, H Karstoft
Computers in biology and medicine 115, 103494, 2019
Unsuperpoint: End-to-end unsupervised interest point detector and descriptor
PH Christiansen, MF Kragh, Y Brodskiy, H Karstoft
arXiv preprint arXiv:1907.04011, 2019
Embryo selection with artificial intelligence: how to evaluate and compare methods?
MF Kragh, H Karstoft
Journal of assisted reproduction and genetics 38 (7), 1675-1689, 2021
Object detection and terrain classification in agricultural fields using 3D lidar data
M Kragh, RN Jørgensen, H Pedersen
International conference on computer vision systems, 188-197, 2015
Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences
J Berntsen, J Rimestad, JT Lassen, D Tran, MF Kragh
Plos one 17 (2), e0262661, 2022
Automatic behaviour analysis system for honeybees using computer vision
GJ Tu, MK Hansen, P Kryger, P Ahrendt
Computers and Electronics in Agriculture 122, 10-18, 2016
FieldSAFE: Dataset for Obstacle Detection in Agriculture
MF Kragh, P Christiansen, MS Laursen, M Larsen, KA Steen, O Green, ...
Sensors 17 (11), 2017
Multimodal obstacle detection in unstructured environments with conditional random fields
M Kragh, J Underwood
Journal of Field Robotics 37 (1), 53-72, 2020
Development and validation of deep learning based embryo selection across multiple days of transfer
J Theilgaard Lassen, M Fly Kragh, J Rimestad, M Nygård Johansen, ...
Scientific reports 13 (1), 4235, 2023
Multi-modal detection and mapping of static and dynamic obstacles in agriculture for process evaluation
T Korthals, M Kragh, P Christiansen, H Karstoft, RN Jørgensen, U Rückert
Frontiers in Robotics and AI 5, 28, 2018
Platform for evaluating sensors and human detection in autonomous mowing operations
P Christiansen, M Kragh, KA Steen, H Karstoft, RN Jørgensen
Precision agriculture 18, 350-365, 2017
Predicting embryo viability based on self-supervised alignment of time-lapse videos
MF Kragh, J Rimestad, JT Lassen, J Berntsen, H Karstoft
IEEE Transactions on Medical Imaging 41 (2), 465-475, 2021
Advanced sensor platform for human detection and protection in autonomous farming
P Christiansen, M Kragh, KA Steen, H Karstoft, RN Jørgensen
European Conference on Precision Agriculture 10, 291-298, 2015
Does embryo categorization by existing artificial intelligence, morphokinetic or morphological embryo selection models correlate with blastocyst euploidy rates?
K Kato, S Ueno, J Berntsen, MF Kragh, T Okimura, T Kuroda
Reproductive BioMedicine Online 46 (2), 274-281, 2023
Multi-modal obstacle detection and evaluation of occupancy grid mapping in agriculture
M Kragh, P Christiansen, T Korthals, T Jungeblut, H Karstoft, ...
International Conference on Agricultural Engineering, 2016
Lidar-based obstacle detection and recognition for autonomous agricultural vehicles
MF Kragh
Department of Engineering, Aarhus University, 2018
Unsuperpoint: End-to-end unsupervised interest point detector and descriptor. arXiv 2019
PH Christiansen, MF Kragh, Y Brodskiy, H Karstoft
arXiv preprint arXiv:1907.04011, 0
Towards inverse sensor mapping in agriculture
T Korthals, M Kragh, P Christiansen, U Rückert
arXiv preprint arXiv:1805.08595, 2018
Sparse-to-dense depth completion in precision farming
S Farkhani, MF Kragh, PH Christiansen, RN Jørgensen, H Karstoft
Proceedings of the 3rd International Conference on Vision, Image and Signal …, 2019
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