| Kinect depth sensor evaluation for computer vision applications MR Andersen, T Jensen, P Lisouski, AK Mortensen, MK Hansen, ... Aarhus University, 1-37, 2012 | 310 | 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 | 101 | 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 | 90 | 2019 |
| 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 | 73 | 2015 |
| FieldSAFE: Dataset for Obstacle Detection in Agriculture MF Kragh, P Christiansen, MS Laursen, M Larsen, KA Steen, O Green, ... Sensors 17 (11), 2017 | 70 | 2017 |
| 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 | 68 | 2016 |
| 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 | 67 | 2021 |
| 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 | 63 | 2022 |
| Multimodal obstacle detection in unstructured environments with conditional random fields M Kragh, J Underwood Journal of Field Robotics 37 (1), 53-72, 2020 | 28 | 2020 |
| 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 | 18 | 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 | 18 | 2017 |
| 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 | 17* | 2023 |
| 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 | 13 | 2015 |
| 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 | 11 | 2021 |
| 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 | 10 | 2023 |
| Multimodal obstacle detection and evaluation of occupancy grid mapping in agriculture. M Kragh, P Christiansen, T Korthals, T Jungeblut, H Karstoft, ... | 9 | 2016 |
| Lidar-based obstacle detection and recognition for autonomous agricultural vehicles MF Kragh Department of Engineering, Aarhus University, 2018 | 6 | 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 | 6 | |
| Towards inverse sensor mapping in agriculture T Korthals, M Kragh, P Christiansen, U Rückert arXiv preprint arXiv:1805.08595, 2018 | 5 | 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 | 3 | 2019 |
Peter Hviid ChristiansenPhD, Department of Engineering, Aarhus Universityeng.au.dk의 이메일 확인됨
