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Results: 8
Number of items: 8
  • Open Access
    Groenendijk, R., Dorst, L., & Gevers, T. (2024). HaarNet: Large-Scale Linear-Morphological Hybrid Network for RGB-D Semantic Segmentation. In S. Brunetti, A. Frosini, & S. Rinaldi (Eds.), Discrete Geometry and Mathematical Morphology: Third International Joint Conference, DGMM 2024, Florence, Italy, April 15–18, 2024 : proceedings (pp. 242–254). (Lecture Notes in Computer Science; Vol. 14605). Springer. https://doi.org/10.1007/978-3-031-57793-2_19
  • Sietsma, A., Groenendijk, R., & Biesbroek, R. (2023, September 26). Data for Progress on Climate Action: a Multilingual Machine Learning Analysis of the Global Stocktake [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8379988
  • Open Access
    Groenendijk, R. W. (2023). Going into depth: Learning morphological aspects in data modalities using neural networks. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Groenendijk, R., Dorst, L., & Gevers, T. (2023). Geometric Back-Propagation in Morphological Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 14045-14051. https://doi.org/10.1109/TPAMI.2023.3290615
  • Open Access
    Groenendijk, R., Dorst, L., & Gevers, T. (2022). MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs. In The 33rd British Machine Vision Conference Proceedings: BMVC 2022 : 21st-24th November 2022, London, UK Article 56 BMVA Press. https://doi.org/10.48550/arXiv.2211.14037
  • Groenendijk, R., Karaoglu, S., Gevers, T., & Mensink, T. (2021). Multi-Loss Weighting with Coefficient of Variations. In 2021 IEEE Winter Conference on Applications of Computer Vision: proceedings : 5-9 January 2021, virtual event (pp. 1468-1477). (WACV). IEEE Computer Society. https://doi.org/10.1109/WACV48630.2021.00151
  • Open Access
    Knott, M., & Groenendijk, R. (2021). Towards Mesh-based Deep Learning for Semantic Segmentation in Photogrammetry. In N. Paparoditis, C. Mallet, F. Lafarge, M. Y. Yang, A. Yilmaz, J. D. Wegner, F. Remondino, T. Fuse, & I. Toschi (Eds.), XXIV ISPRS Congress "Imaging today, foreseeing tomorrow": Commission II (pp. 59-66). (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences; Vol. V-2-2021). ISPRS. https://doi.org/10.5194/isprs-annals-V-2-2021-59-2021
  • Open Access
    Groenendijk, R., Karaoglu, S., Gevers, T., & Mensink, T. (2020). On the benefit of adversarial training for monocular depth estimation. Computer Vision and Image Understanding, 190, Article 102848. https://doi.org/10.1016/j.cviu.2019.102848
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