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Conference Paper
W. D'haes, Van Dyck, D., and Rodet, X., “Control Parameter Estimation for a Physical Model of a Trumpet Using Pattern Recognition”, in IEEE Workshop on Model Based Processing and Coding of Audio (MPCA), Leuven, Belgium, 2002.
L. F. Alves Pereira, Dabravolski, A., Tsang, I. R., Cavalcanti, G. D. C., and Sijbers, J., “Conveyor belt X-ray CT using Domain Constrained Discrete Tomography”, in Sibgrapi conference on Graphics, Patterns and Images, 2014, pp. 290 - 297.PDF icon Download paper (3.45 MB)
J. Sijbers, Van Camp, N., Leemans, A., den Dekker, A. J., Verhoye, M., and Van Der Linden, A., “Coregistration of Micro-MRI, microCT and microPET”, in Workshop on non Invasive 3D Microscopy, University of Antwerp, Belgium, 2004, p. 16.
N. Francken, Sanctorum, J., Huyge, B., Sijbers, J., and De Beenhouwer, J., “Correcting for focal spot drift in edge illumination X-ray phase contrast imaging”, in 14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium, 2025.
F. Danckaers, Huysmans, T., Lacko, D., Ledda, A., Verwulgen, S., Van Dongen, S., and Sijbers, J., “Correspondence Preserving Elastic Surface Registration with Shape Model Prior”, in International Conference of Pattern Recognition, 2014, vol. 22, pp. 2143-2148.PDF icon paper (550.57 KB)
Z. Bnoulkacem, Vlaminck, M., Morocho, G. Raimundo L., Zivariadab, H., Zahiri, Z., Koirala, B., Platisa, L., Luong, H., Scheunders, P., Mangialetto, F., and Bufi, I., “Corrosion detection using hyperspectral VNIR and SWIR imaging for drone-based inspection of electrical pylons”, in EUROCORR 2023, European Corrosion Congress, Proceedings, 2023, p. 2.PDF icon exhibit_17_corrosion_detection_using_hyperspectral_vnir_and_swir_imaging.pdf (138.17 KB)
S. Livens, Anthonis, A., Mahy, M., and Scheunders, P., “Cross-media tonal mapping model obtained from psychometric experiments”, in Proc. SPIE 5008: Color Imaging, Device- Independent Color, Color Hardcopy, and Graphic Arts VII , Santa Clara, CA, 2003, pp. 14-23.
J. Verstraete, Francken, N., Sijbers, J., and De Beenhouwer, J., “Cycloidal Acquisition for Inline Edge Illumination X-ray Phase Contrast Computed Tomography: A Simulation Study”, in 15th Conference on Industrial Computed Tomography (iCT), Linz, Austria, 2026, vol. 31.PDF icon Download paper (22.22 MB)
J. Sanctorum, Sijbers, J., and De Beenhouwer, J., “Dark field sensitivity in single mask edge illumination lung imaging”, in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France, 2021, pp. 775-778.
K. J. Batenburg and Sijbers, J., “DART: a fast heuristic algebraic reconstruction algorithm for discrete tomography”, in Proceedings of the IEEE International Conference on Image Processing (ICIP), San Antonio, Texas, USA, 2007.PDF icon Download paper (415.32 KB)
R. Pinho, Tournoy, K. G., Gosselin, R., and Sijbers, J., “A Decision Support System for the Treatment of Tracheal Stenosis”, in Proc. of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM), Istanbul, 2010, pp. 72-76.
B. Rasti, Koirala, B., Scheunders, P., and Chanussot, J., “Deep Blind Unmixing using Minimum Simplex Convolutional Network”, in IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 28-31.PDF icon misicnet_igarss2022.pdf (1.29 MB)
L. F. Alves Pereira, Van Nieuwenhove, V., De Beenhouwer, J., and Sijbers, J., “A Deep Convolutional Framelet Network based on Tight Steerable Wavelet: application to sparse-view medical tomosynthesis”, in International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2021.
C. Bossuyt, den Dekker, A. J., Iuso, D., Le Hoang, T., Escoda, J., Costin, M., Sijbers, J., and De Beenhouwer, J., “Deep image prior for sparse-view reconstruction in static, rectangular multi-source x-ray CT systems for cargo scanning”, in SPIE Developments in X-Ray Tomography XV, 2024, vol. 13152.
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.
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., and Sijbers, J., “The Deep Steerable Convolutional Framelet Network for Suppressing Directional Artifacts in X-ray Tomosynthesis”, in 31st European Signal Processing Conference, EUSIPCO, 2023.
M. Yosifov, Weinberger, P., Reiter, M., Fröhler, B., De Beenhouwer, J., Sijbers, J., Kastner, J., and Heinzl, C., “Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations”, in e-Journal of Nondestructive Testing, 2023, vol. 28, no. 3.PDF icon Download paper (2.37 MB)
J. Renders, Shafieizargar, B., Verhoye, M., De Beenhouwer, J., den Dekker, A. J., and Sijbers, J., “DELTA-MRI: Direct deformation Estimation from LongiTudinally Acquired k-space data”, in IEEE International Symposium on Biomedical Imaging, 2023, pp. 1-4.
J. Rajan, Jeurissen, B., Sijbers, J., and Kannan, K., “Denoising Magnetic Resonance Images using Fourth Order Complex Diffusion”, in 13th International Machine Vision and Image Processing Conference, Dublin, Ireland, 2009, pp. 123-127.
A. Karami, Heylen, R., and Scheunders, P., “Denoising Of Hyperspectral Images Using Shearlet Transform And Fully Constrained Least Squares Unmixing”, in 8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing, Los Angeles, USA, 2016.
P. Scheunders, “Denoising of multispectral images using wavelet thresholding”, in Proc. SPIE conference on Image and Signal Processing for Remote Sensing IX, part of the International Symposium on Remote Sensing, Barcelona, Spain, 2003, pp. 28-35.
S. Delputte, Leemans, A., Fieremans, E., De Deene, Y., D'Asseler, Y., Lemahieu, I., Achten, E., Sijbers, J., and Van de Walle, R., “Density Regularized Fiber Tractography of the Brain White Matter using Diffusion Tensor MRI”, in 13th Scientific Meeting - International Society for Magnetic Resonance in Medicine, Miami, USA, 2005, p. 1309.
T. Hu, Liu, N., Li, W., Tao, R., Zhang, F., and Scheunders, P., “Destriping Hyperspectral Imagery By Adaptive Anisotropic Total Variation And Truncated Nuclear Norm”, in Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2021.
A. J. den Dekker and Sijbers, J., “Detection of brain activation from magnitude fMRI data using a generalized likelihood ratio test”, in Proceedings of the 12th European Signal Processing Conference, Vienna, Austria, 2004, pp. 233-236.

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