Publications

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H
T. Huysmans, Danckaers, F., Vleugels, J., Lacko, D., De Bruyne, G., Verwulgen, S., and Sijbers, J., “Multi-patch B-Spline Statistical Shape Models for CAD-Compatible Digital Human Modeling”, in Advances in Human Factors in Simulation and Modeling, Cham, 2019, pp. 179–189.
T. Huysmans, Sijbers, J., and Verdonk, B., “Statistical shape models for tubular objects”, in Proceedings of IEEE BENELUX/DSP Valley Signal Processing Symposium (SPS-DARTS), Antwerp, Belgium, 2006, pp. 155-158.PDF icon Full text (1.06 MB)
T. Huysmans, Sijbers, J., and Verstreken, F., “Orthosis”, U.S. Patent WO/2016/1812822016.
T. Huysmans, Bernat, A., Sijbers, J., Parizel, P. M., Van Glabbeek, F., and Verdonk, B., “Shape Analysis of the Human Clavicle for the Development of a Set of osteosynthesis Plates”, in Belgian Day on Biomedical Engineering - IEEE/EMBS Benelux Symposium, Brussels, Belgium, 2006, pp. 128-129.PDF icon Full text (103.17 KB)
I
D. Iuso, Chatterjee, S., Heylen, R., Cornelissen, S., De Beenhouwer, J., and Sijbers, J., “Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing”, in SPIE Optical Engineering: Developments in X-Ray Tomography XIV , 2022, vol. 12242, p. 122421K.PDF icon Download paper (protected) (1.79 MB)
D. Iuso, Chatterjee, S., Cornelissen, S., Verhees, D., De Beenhouwer, J., and Sijbers, J., “Voxel-wise segmentation for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural models”, Applied Intelligence, vol. 54, pp. 13160–13177, 2024.PDF icon Download paper (2.48 MB)
D. Iuso, Paramonov, P., De Beenhouwer, J., and Sijbers, J., “PACS: Projection-driven with Adaptive CADs X-ray Scatter compensation for additive manufacturing inspection”, Precision Engineering, vol. 90, pp. 108-121, 2024.PDF icon Download paper (4.28 MB)
D. Iuso, Nazemi, E., Six, N., De Samber, B., De Beenhouwer, J., and Sijbers, J., “CAD-based scatter compensation for polychromatic reconstruction of additive manufactured parts”, in IEEE ICIP, 2021, pp. 2948-2952.
D. Iuso, “Ensuring Flawlessness in Additive Manufacturing: Advances in X-ray Inspection Techniques for Efficient Defect Detection”, 2024.PDF icon Download thesis (31.17 MB)
D. Iuso, Paramonov, P., De Beenhouwer, J., and Sijbers, J., “Practical multi-mesh registration for few-view poly-chromatic X-ray inspection”, Journal of Non-destructive Testing, vol. 43, 2024.PDF icon Download paper (6.94 MB)
J
J. - P. Jacobs, Thoonen, G., Tuia, D., Camps-Valls, G., Kempeneers, P., and Scheunders, P., “Spectral adaptation of hyperspectral flight lines using VHR contextual information”, in Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, Quebec City, QC, Canada, 2014, pp. 2953-2956.
J. - P. Jacobs, Thoonen, G., Tuia, D., Camps-Valls, G., Haest, B., and Scheunders, P., “Domain adaptation with Hidden Markov Random Fields”, in Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, Melbourne, VIC, Australia, 2013, pp. 3112-3115.
W. Jacquet, Nyssen, E., Sun, Y., De Munter, S., Sijbers, J., and Politis, C., “Alveolar Nerve Unfolding Technique for Synoptic Analysis: Visualization and Quantification of Inferior Alveolar Nerve Tracings in Three-dimensional Cone-Beam Computed Tomography.”, The Journal of craniofacial surgery, vol. 24, no. 4, pp. e374-7, 2013.
K. Vinay Jambhali, Koirala, B., Bnoulkacem, Z., and Scheunders, P., “Soil Moisture Content Estimation from Hyperspectral Remote Sensing Data”, in 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2023, pp. 1-5.PDF icon soil_ketaki.pdf (580.15 KB)
K. Vinay Jambhali, Koirala, B., Bnoulkacem, Z., and Scheunders, P., “Soil Moisture Content Estimation From Hyperspectral Remote Sensing Data”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 22231-22240, 2025.PDF icon soil_moisture_content_estimation_from_hyperspectral_remote_sensing_data_1_.pdf (9.35 MB)
E. Janssens, Sijbers, J., Dierick, M., and De Beenhouwer, J., “Fast detection of cracks in ultrasonically welded parts by inline X-ray inspection”, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019.
E. Janssens, De Beenhouwer, J., Van Dael, M., Verboven, P., Nicolai, B., and Sijbers, J., “Neural Network Based X-Ray Tomography for Fast Inspection of Apples on a Conveyor Belt”, in IEEE International Conference on Image Processing, 2015, pp. 917-921.
E. Janssens, Pelt, D., De Beenhouwer, J., Van Dael, M., Verboven, P., Nicolai, B., and Sijbers, J., “Fast Neural Network Based X-Ray Tomography of Fruit on a Conveyor Belt”, Chemical Engineering Transactions, vol. 44, pp. 181-186, 2015.
E. Janssens, De Beenhouwer, J., Sanctorum, J., Senck, S., Heinzl, C., and Sijbers, J., “Dual axis Dark Field Contrast Tomography for visualisation of scattering directions in a CFRP sample”, 4th Conference on X-ray and Neutron Phase Imaging with Gratings. Zürich, Switzerland, pp. 79-80, 2017.PDF icon Download poster (729.94 KB)PDF icon Download abstract (116.29 KB)
E. Janssens, De Beenhouwer, J., Van Dael, M., De Schryver, T., Van Hoorebeke, L., Verboven, P., Nicolai, B., and Sijbers, J., “Neural network Hilbert transform based filtered backprojection for fast inline X-ray inspection”, Measurement Science and Technology, vol. 29, no. 3, 2018.PDF icon Download paper (3.4 MB)
E. Janssens, Senck, S., Heinzl, C., Kastner, J., De Beenhouwer, J., and Sijbers, J., “Fast Reconstruction of CFRP X-ray Images based on a Neural Network Filtered Backprojection Approach”, in 7th Conference on Industrial Computed Tomography, Leuven, Belgium, 2017.PDF icon Download paper (345.06 KB)
E. Janssens, “Advances in X-ray reconstruction algorithms for limited data problems in conventional and non-conventional projection geometries”, 2018.PDF icon Download thesis (17.29 MB)
E. Janssens, Alves Pereira, L. F., De Beenhouwer, J., Tsang, I. R., Van Dael, M., Verboven, P., Nicolai, B., and Sijbers, J., “Fast inline inspection by neural network based filtered backprojection: Application to apple inspection”, Case Studies in Nondestructive Testing and Evaluation, vol. 6, pp. 14–20, 2016.PDF icon Download paper (726.63 KB)
P. Jedrasik, Garcia, J., De Boeck, B., and Van Dyck, D., “Optimal filtering versus regularization methods in the Fourier precompensation based proximity neurocorrection in electron beam lithography”, Microelectronic Engineering, vol. 41, pp. 195-198, 1998.
I. O. Jelescu, Grussu, F., Ianus, A., Hansen, B., Barrett, R. L. C., Aggarwal, M., Michielse, S., Nasrallah, F., Syeda, W., Wang, N., Veraart, J., Roebroeck, A., Bagdasarian, A. F., Eichner, C., Sepehrband, F., Zimmermann, J., Soustelle, L., Bowman, C., Tendler, B. C., Hertanu, A., Jeurissen, B., Verhoye, M., Frydman, L., van de Looij, Y., Hike, D., Dunn, J. F., Miller, K., Landman, B. A., Shemesh, N., Anderson, A., McKinnon, E., Farquharson, S., Dell'Acqua, F., Pierpaoli, C., Drobnjak, I., Leemans, A., Harkins, K. D., Descoteaux, M., Xu, D., Huang, H., Santin, M. D., Grant, S. C., Obenaus, A., Kim, G. S., Wu, D., Le Bihan, D., Blackband, S. J., Ciobanu, L., Fieremans, E., Bai, R., Leergaard, T. B., Zhang, J., Dyrby, T. B., G. Johnson, A., Cohen‐Adad, J., Budde, M. D., and Schilling, K. G., “Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging”, Magnetic Resonance in Medicine, vol. 93, no. 6, pp. 2507 - 2534, 2025.

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