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2021
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “Spectral Unmixing Using Deep Convolutional Encoder-Decoder”, in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 3829-3832.PDF icon igarss2021.pdf (659.27 KB)
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “Spectral Unmixing Using Deep Convolutional Encoder-Decoder”, in IGARSS 2021, International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 2021.
F. Danckaers, Van Houtte, J., Booth, B. G., Verstreken, F., and Sijbers, J., “Statistical shape and pose model of the forearm for custom splint design”, in IEEE International Symposium on Biomedical Imaging (ISBI), 2021.
P. Paramonov, Lumbeeck, L. - P., Sijbers, J., and De Beenhouwer, J., “A study of terahertz beam simulation with ray tracing for computed tomography”, in 2021 OSA Imaging and Applied Optics Congress, 2021.
D. Frenkel, De Beenhouwer, J., and Sijbers, J., “Tabu-DART: an dynamic update strategy for the Discrete Algebraic Reconstruction Technique based on Tabu-search”, in International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2021.
K. Rafiezadeh Sahi, Rasti, B., Ghamisi, P., Scheunders, P., and Gloaguen, R., “When is the right time to apply denoising?”, in IGARSS 2021, International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 2021.
2020
P. Paramonov, Lumbeeck, L. - P., De Beenhouwer, J., and Sijbers, J., “Accurate terahertz simulation with ray tracing incorporating beam shape and refraction”, in IEEE ICIP , 2020, pp. 3035-3039.PDF icon Download paper (190.62 KB)
D. Frenkel, De Beenhouwer, J., and Sijbers, J., “An adaptive probability map for the Discrete Algebraic Reconstruction Technique”, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020, pp. 1-6.
J. Renders, Sijbers, J., and De Beenhouwer, J., “Adjoint pairs of image warping operators for motion modeling in 4D-CT”, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020.PDF icon Download paper (617.11 KB)
B. Auer, Kalluri, K., Abayazeed, A. H., De Beenhouwer, J., Zeraatkar, N., Lindsay, C., Momsen, N., R. Richards, G., May, M., Kupinski, M. A., Kuo, P. H., Furenlid, L. R., and King, M. A., “Aperture size selection for improved brain tumor detection and quantification in multi-pinhole 123I-CLINDE SPECT imaging”, in IEEE Nuclear Science Symposium and Medical Imaging Conference, Boston, USA (2020), 2020.
V. Nguyen, De Beenhouwer, J., Bazrafkan, S., Hoang, A. - T., Van Wassenbergh, S., and Sijbers, J., “BeadNet: a network for automated spherical marker detection in radiographs for geometry calibration”, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020, pp. 518-521.PDF icon Download paper (2.16 MB)
M. Ljubenović, Bazrafkan, S., De Beenhouwer, J., and Sijbers, J., “CNN-based Deblurring of Terahertz Images”, in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), 2020, vol. 4, pp. 323-330.PDF icon Download paper (16.31 MB)
T. Peeters, Vleugels, J., Verwulgen, S., Danckaers, F., Huysmans, T., Sijbers, J., and De Bruyne, G., “A Comparative Study Between Three Measurement Methods to Predict 3D Body Dimensions Using Shape Modelling”, in Advances in Additive Manufacturing, Modeling Systems and 3D Prototyping, Cham, 2020, vol. 975, pp. 464–470.
A. Presenti, Bazrafkan, S., Sijbers, J., and De Beenhouwer, J., “Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT”, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020.
L. F. Alves Pereira, De Beenhouwer, J., Kastner, J., and Sijbers, J., “Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks”, in ICTAI 2020, 2020.PDF icon Download paper (2.5 MB)
N. Six, Renders, J., Sijbers, J., and De Beenhouwer, J., “Newton-Krylov Methods For Polychromatic X-Ray CT”, in 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, 2020, pp. 3045-3049.
J. Sanctorum, Nguyen, V., Sijbers, J., Van Wassenbergh, S., and Dirckx, J. J. J., “Projection angle adapted distortion correction in high-speed image-intensifier based tomography”, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020.PDF icon Download paper (818.64 KB)
L. - P. Lumbeeck, Paramonov, P., Sijbers, J., and De Beenhouwer, J., “The Radon Transform for Terahertz Computed Tomography Incorporating the Beam Shape”, in IEEE ICIP, 2020, pp. 3040-3044.PDF icon Download paper (692.64 KB)
M. Nauwynck, Bazrafkan, S., Van Heteren, A., De Beenhouwer, J., and Sijbers, J., “Ring Artifact Reduction in Sinogram Space Using Deep Learning”, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020.PDF icon Download paper (2.49 MB)
S. Bazrafkan, Van Nieuwenhove, V., Soons, J., De Beenhouwer, J., and Sijbers, J., “Ringing Artefact Removal From Sparse View Tomosynthesis using Deep Neural Networks”, in The 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020, pp. 380-383.PDF icon Download paper (570.79 KB)
2019
S. Mukunthan, Vleugels, J., Huysmans, T., Mayor, T. Sotto, and De Bruyne, G., “A 3D Printed Thermal Manikin Head for Evaluating Helmets for Convective and Radiative Heat Loss”, in Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) Volume VII, 2019, vol. VII, pp. 592–602.
J. Van Houtte, Stanković, K., Booth, B. G., Danckaers, F., Bertrand, V., Verstreken, F., Sijbers, J., and Huysmans, T., “An Articulating Statistical Shape Model of the Human Hand”, in Advances in Human Factors in Simulation and Modeling (AHFE 2018), Cham, 2019, vol. 780, pp. 433–445.
F. Danckaers, Scataglini, S., Haelterman, R., Van Tiggelen, D., Huysmans, T., and Sijbers, J., “Automatic Generation of Statistical Shape Models in Motion”, in Advances in Human Factors in Simulation and Modeling (AHFE 2018), Cham, 2019, vol. 780, pp. 170–178.
A. Presenti, Sijbers, J., den Dekker, A. J., and De Beenhouwer, J., “CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images”, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019, pp. 1-5.PDF icon ict2019_full_paper_55.pdf (216.2 KB)
J. Van Houtte, Bazrafkan, S., Vandenberghe, F., Zheng, G., and Sijbers, J., “A Deep Learning Approach to Horse Bone Segmentation from Digitally Reconstructed Radiographs”, in International Conference on Image Processing Theory, Tools, and Applications, 2019.

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