Publications

Export 1324 results:
Author [ Type(Asc)] Year
Filters: Sparse-unmixing-using-deep-convolutional-networks is   [Clear All Filters]
Conference Paper
W. Van Hecke, Leemans, A., Sijbers, J., Parizel, P. M., and Van Goethem, J., “Diffusion Tensor Tractoghraphy Reaveals Spinal Cord Alterations in Patients with Multiple Sclerosis”, in 32nd Congress of the Europea Society of NeuroRadiolog, 2007, p. 129.
P. M. Parizel and Van Hecke, W., “Diffusion Tensor Imaging: Is it really useful?”, in 32nd Congress of the Europea Society of NeuroRadiolog, 2007.
Z. Mai, Verhoye, M., Van Der Linden, A., and Sijbers, J., “Diffusion Tensor Images Upsampling: a Registration-based Approach”, in 13th International Machine Vision and Image Processing Conference, Dublin, Ireland, 2009, vol. 13, pp. 36-40.PDF icon Download paper (808.21 KB)
Z. Mai, Verhoye, M., and Sijbers, J., “Diffusion Tensor Images Edge-Directed Interpolation”, in International Symposium on Biomedical Imaging 2010, 2010, pp. 732-735.
W. Van Hecke, Leemans, A., De Brabander, N., Laridon, A., Claeys, K., Ceulemans, B., Van Goethem, J., Parizel, P. M., and Sijbers, J., “Diffusion Tensor Fiber Tracking Reveals Probst Bundles in Patients with Agenesis of the Corpus Callosum”, in 32nd Congress of the Europea Society of NeuroRadiolog, 2007, p. 135.
W. Van Hecke, Leemans, A., De Brabander, N., Laridon, A., Ceulemans, B., Sijbers, J., and Parizel, P. M., “Diffusion tensor fiber tracking in patients with agenesis of the corpus callosum”, in 23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology, Warsaw, Poland, 2006, pp. 34-35.
T. Roine, Pietilä, J., Kaartinen, J., Blanz, P., and Rantala, P., “Development of a Machine Vision System to Monitor a Grinding Mill Prototype”, in 12th European Symposium on Comminution and Classification (ESCC 2009), 2009.
S. Verwulgen, Lacko, D., Justine, H., Kustermans, S., Moons, S., Thys, F., Zelck, S., Vaes, K., Huysmans, T., Vleugels, J., and Truijen, S., “Determining Comfortable Pressure Ranges for Wearable EEG Headsets”, in Advances in Human Factors in Wearable Technologies and Game Design, Cham, 2019, pp. 11–19.
M. Shahrimie Asaari, Mertens, S., Dhondt, S., Wuyts, N., and Scheunders, P., “Detection of plant responses to drought using close-range hyperspectral imaging in a high-throughput phenotyping platform”, in IEEE Whispers 2018, Workshop on Hyperspectral Image and Signal Processing, Amsterdam, 23-26 September , 2018.
D. Meersman, Scheunders, P., and Van Dyck, D., “Detection of microcalcifications using non-linear filtering”, in Proc. EUSIPCO'98, European Signal Processing Conference, 1998, pp. 2465-2468.
D. Meersman, Scheunders, P., and Van Dyck, D., “Detection of microcalcifications using neural networks”, in Digital Mammography, 1996, pp. 287-290.
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.
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.
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.
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.
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.
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.
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.
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)
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.
H. Q. Nguyen, Nguyen, B. T., Dong, T. Q., Ngo, D. T., and Nguyen, A. - T., “Deep Q-Learning with Multiband Sensing for Dynamic Spectrum Access”, in IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Seoul, Korea, 2018.
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.
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.
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.
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.

Pages