Publications

Export 1328 results:
Author [ Type(Desc)] Year
Filters: Sparse-unmixing-using-deep-convolutional-networks is   [Clear All Filters]
Journal Article
A. Presenti, Liang, Z., Alves Pereira, L. F., Sijbers, J., and De Beenhouwer, J., “Fast and accurate pose estimation of additive manufactured objects from few X-ray projections”, Expert Systems With Applications, vol. 213, no. 118866, pp. 1-10, 2023.
W. Van Aarle, Palenstijn, W. J., Cant, J., Janssens, E., Bleichrodt, F., Dabravolski, A., De Beenhouwer, J., Batenburg, K. J., and Sijbers, J., “Fast and Flexible X-ray Tomography Using the ASTRA Toolbox”, Optics Express, vol. 24, pp. 25129-25147, 2016.PDF icon Download paper (8.34 MB)
S. Van der Jeught, Sijbers, J., and Dirckx, J. J. J., “Fast Fourier-based phase unwrapping on the graphics processing unit in real-time imaging applications”, Journal of Imaging, vol. 1, pp. 31-44, 2015.PDF icon Download paper (1.35 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)
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.
Z. Zahiri, Laefer, D., and Gowen, A., “The feasibility of short-wave infrared spectrometry in assessing water-to-cement ratio and density of hardened concrete”, Construction and Building Materials, 2018.
F. Tajdari, Huysmans, T., Yang, Y., and Song, Y., “Feature preserving non-rigid iterative weighted closest point and semi-curvature registration”, IEEE Transactions on Image Processing, 2022.
B. Huyge, Sanctorum, J., Jeurissen, B., De Beenhouwer, J., and Sijbers, J., “Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers using Constrained Spherical Deconvolution”, Polymers, vol. 15, no. 13, p. 2887, 2023.PDF icon Download paper (2.95 MB)
B. De Samber, Renders, J., Elberfeld, T., Maris, Y., Sanctorum, J., Six, N., Liang, Z., De Beenhouwer, J., and Sijbers, J., “FleXCT: a Flexible X-ray CT scanner with 10 degrees of freedom”, Optics Express, vol. 29, no. 3, pp. 3438-3457, 2021.PDF icon Download paper (14.25 MB)
F. Danckaers, Stanković, K., Huysmans, T., Booth, B. G., and Sijbers, J., “Foot shape assessment techniques for orthotic and footwear applications: a methodological literature review”, Frontiers in Bioengineering and Biotechnology, vol. 12, pp. 1-15, 2024.PDF icon Download paper (1.99 MB)
L. Vanacken, Pinho, R., Sijbers, J., and Coninx, K., “Force Feedback to Assist Active Contour Modelling for Tracheal Stenosis Segmentation”, Advances in Human-Computer Interaction, vol. 2012, 2012.PDF icon Download paper (3.6 MB)
A. Van Tongel, De Wilde, L., Shimamura, Y., Sijbers, J., and Huysmans, T., “Fracture patterns in midshaft clavicle fractures”, Acta Orthop Belg , vol. 87, no. 3, pp. 501-507, 2021.PDF icon Download paper (830.55 KB)
D. Van Dyck, Bettens, E., Sijbers, J., Op de Beeck, M., den Dekker, A. J., and van den Bos, A., “From High Resolution Image to Atomic Structure: how fare are we?”, Scanning Microscopy, Special Issue on Image Processing, vol. 11, pp. 467-478, 1997.
R. Heylen, Burazerovic, D., and Scheunders, P., “Fully constrained least-squares spectral unmixing by simplex projection”, IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4112-4122, 2011.PDF icon PDF (1.11 MB)Package icon Matlab code (1.93 KB)
T. Boumans, Vignal, C., Smolders, A., Sijbers, J., Verhoye, M., Van Audekerke, J., and Van Der Linden, A., “Functional magnetic resonance imaging in zebra finch discerns the neural substrate involved in segregation of conspecific song from background noise”, Journal of Neurophysiology, vol. 99, pp. 931-938, 2008.PDF icon Download full paper (832.59 KB)
P. Scheunders and De Backer, S., “Fusion and merging of multispectral images using multiscale fundamental forms”, Journal of the Optical Society of America A, vol. 18, pp. 2468-2477, 2001.
R. Luo, Liao, W., Zhang, H., Zhang, L., Pi, Y., Scheunders, P., and Philips, W., “Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow Mixed Remote Sensing Scene”, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 8, pp. 3768-3781, 2017.
Z. H. Nezhad, Karami, A., Heylen, R., and Scheunders, P., “Fusion of Hyperspectral and Multispectral Images Using Spectral Unmixing and Sparse Coding”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 6, pp. 2377-2389, 2016.
N. Six, Renders, J., Sijbers, J., and De Beenhouwer, J., “Gauss-Newton-Krylov for Reconstruction of Polychromatic X-ray CT Images”, IEEE Transactions on Computational Imaging, vol. 7, pp. 1304-1313, 2021.
A. De Luca, Ianus, A., Leemans, A., Palombo, M., Shemesh, N., Zhang, H., Alexander, D. C., Nilsson, M., Froeling, M., Biessels, G. - J., Zucchelli, M., Frigo, M., Albay, E., Sedlar, S., Alimi, A., Deslauriers-Gauthier, S., Deriche, R., Fick, R., Afzali, M., Pieciak, T., Bogusz, F., Aja-Fernandez, S., Ozarslan, E., Jones, D. K., Chen, H., Jin, M., Zhang, Z., Wang, F., Nath, V., Parvathaneni, P., Morez, J., Sijbers, J., Jeurissen, B., Shreyas,, Fadnavis,, Endres, S., Rokem, A., Garyfallidis, E., Sanchez, I., Prchkovska, V., Rodrigues, P., Landman, B. A., and Schilling, K. G., “On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge”, NeuroImage, vol. 240, no. 118367, 2021.
J. Sijbers and den Dekker, A. J., “Generalized likelihood Ratio tests for complex fMRI data: a simulation study”, IEEE Transactions on Medical Imaging, vol. 24, pp. 604-611, 2005.PDF icon Download paper (377.11 KB)
K. J. Batenburg and Sijbers, J., “Generic iterative subset algorithms for discrete tomography”, Discrete Applied Mathematics, vol. 157, pp. 438-451, 2009.PDF icon Download paper (1.41 MB)
P. Kempeneers, De Backer, S., Debruyn, W., and Scheunders, P., “Generic Wavelet-Based Hyperspectral Classification Applied to Vegetation Stress Detection”, IEEE Transactions on Geoscience and Remote Sensing, vol. 43, pp. 610-614, 2005.
P. Scheunders, “A genetic c-means clustering algorithm applied to color image quantization”, Pattern Recognition, vol. 30, pp. 859-866, 1997.
S. Yu, De Backer, S., and Scheunders, P., “Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery”, Pattern Recognition Letters, vol. 23, pp. 183-190, 2002.

Pages