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Conference Paper
T. Elberfeld, De Beenhouwer, J., and Sijbers, J., Fiber assignment by continuous tracking for parametric fiber reinforced polymer reconstruction, in 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2019, vol. 11072.PDF icon Download paper (5.15 MB)
B. Huyge, Jeurissen, B., De Beenhouwer, J., and Sijbers, J., Fiber orientation estimation by constrained spherical deconvolution of the anisotropic edge illumination x-ray dark field signal, in SPIE: Developments in X-Ray Tomography XIV, 2022, vol. 12242, p. 122420V .PDF icon Download paper (956.82 KB)
B. Jeurissen, Leemans, A., Tournier, J. - D., and Sijbers, J., Fiber Tracking on the 'Fiber Cup Phantom' using Constrained Spherical Deconvolution, in MICCAI workshop on Diffusion Modelling and the Fiber Cup (DMFC'09), London, United Kingdom, 2009.
L. Plantagie, Van Aarle, W., Batenburg, K. J., and Sijbers, J., Filtered backprojection using algebraic filters; Application to biomedical micro-CT data, in International Symposium on Biomedical Imaging (ISBI), 2015, pp. 1596-1599.
G. Nagels, Vandervliet, E., Van Hecke, W., Engelborghs, S., hooghe, M. B. D., Cras, P., Parizel, P. M., and De Deyn, P. P., fMRI during PASAT and PVSAT in mild MS, moderate MS and normal volunteers., in 22nd Congress of the European Committee for, Madrid, Spain, 2006.
K. Stanković, Danckaers, F., Booth, B. G., Burg, F., Duerinck, S., Sijbers, J., and Huysmans, T., Foot Abnormality Mapping using Statistical Shape Modelling, in 7th International Conference and Exhibition on 3D Body Scanning Technologies (3DBST), 2016, Lugano, Switzerland, 30 November - 1 December., pp. 70-79.
B. Koirala, Zahiri, Z., Khodadadzadeh, M., and Scheunders, P., Fractional abundance estimation of mixed and compound materials by hyperspectral imaging., in 10th Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Sept 2019, Amsterdam, Netherlands, 2019, pp. pp. 1-5.PDF icon fractional_abundance_estimation_of_mixed_and_compound_materials_by_hyperspectral_imaging.pdf (742.67 KB)
B. De Boeck, Scheunders, P., and Van Dyck, D., From inductive inference to the fundamental equations of measuerement, in Proc. First International Conference on Complex Systems, 2000, pp. 115-122.
F. Danckaers, Huysmans, T., Hallemans, A., De Bruyne, G., Truijen, S., and Sijbers, J., Full Body Statistical Shape Modeling with Posture Normalization, in The 8th International Conference on Applied Human Factors and Ergonomics (AHFE 2017), Los Angeles, California, USA, 2018, vol. 591, pp. 437-448.PDF icon Download paper (1.12 MB)
V. Van Meir, Boumans, T., De Groof, G., Van Audekerke, J., Smolders, A., Scheunders, P., Sijbers, J., Verhoye, M., Balthazart, J., and Van Der Linden, A., Functional Magnetic resonance Imaging meets animal vocal learner, in Third One-Day Symposium of Young Belgian Magnetic Resonance Scientists, Brussels, Belgium, 2004.
R. M. Soleimanzadeh, Karami, A., and Scheunders, P., Fusion of hyperspectral and Lidar images using non-subsampled shearlet transform, in IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27, 2018.
S. Yu and Scheunders, P., Fuzzy markov chains approach to feature selection for high dimensional remote sensing data, in Geoscience and Remote Sensing Symposium, 2001, pp. 3306-3308.
D. H. J. Poot, Van Meir, V., and Sijbers, J., General and Efficient Super-Resolution method for Multi-Slice MRI, in Medical Image Computing and Computer Assisted Intervention, 2010, vol. 13, no. 1, pp. 615-622.PDF icon Download paper (229.84 KB)
J. Sijbers and den Dekker, A. J., Generalized likelihood ratio test for complex fMRI data, in SPIE Medical Imaging: Physiology, Function, and Structure from Medical Images, San Diego, California, USA, 2004, vol. 5369, pp. 652-663.PDF icon Download full paper (251.15 KB)
C. C. Hung, Coleman, T., and Scheunders, P., The genetic algorithm approach and K-means clustering: their role in unsupervised training in image classification, in Proc. IASTED International Conf. On Computer Graphics and Imaging , Halifax, Canada, june 1-3, 1998, pp. 103-106.
P. Scheunders, A genetic approach towards optimal color image quantization, in Proc. ICIP'96, IEEE Internat. Conference on Image Processing , III, Lausanne, september 16-19, 1996, pp. 1031-1034.
S. Yu, Scheunders, P., and De Backer, S., Genetic Feature selection combined with composite fuzzy nearest neighbor classifers for high-dimensional remote sensing data, in Proc. IEEE Intern. Conf. on Systems, Man & Cybernetics, October 8-11, Nashville,TN, 2000, pp. 1912-1916.
S. Yu, De Backer, S., and Scheunders, P., Genetic Feature Selection Combined with Fuzzy K-NN for Hyperspectral Satellite Imagery, in Intelligent Techniques and Soft Computing in Nuclear Science and Engineering, 2000, pp. 281-288.
S. Yu, De Backer, S., and Scheunders, P., Genetic feature selection combined with fuzzy kNN for hyperspectral satellite imagery, in Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International, 2000, pp. 702-704.
P. Scheunders, Genetic optimal quantization of gray-level and color images, in Proceedings ACCV'95, Second Asian Conference on Computer Vision, Singapore, 5-8 december, 1995, pp. 94-98.
A. Leemans, Sijbers, J., Verhoye, M., Van Der Linden, A., and Van Dyck, D., A Geometric Color Scheme for Visualizing Diffusion Tensor Magnetic Resonance Fiber Pathways, in 19th Annual Symposium - Belgian Hospital Physicists Association, Brussels, Belgium, 2004.
M. A. Akhter, Heylen, R., and Scheunders, P., Geometric matched filter for hyperspectral partial unmixing, in IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse, 2014.
O. Melet, Huysmans, T., Hummel, M., and Brunet, P. - M., GPGPU and MIC in accelerated cluster for remote sensed image processing software, in Conference on Big Data from Space (BiDS), 12-14 November, ESRIN, Frascati, Italy, 2014.PDF icon paper (158.32 KB)
S. Livens, Van Roost, C., Scheunders, P., and Van Dyck, D., Granulometric segmentation using a gradient convergence map, in Proc. Scandinavian Conference on Image Analysis , Lappeenranta, Finland, 9-11/6, 1997, pp. 389-396.
R. Heylen, Burazerovic, D., and Scheunders, P., A graph-based method for non-linear unmixing of hyperspectral imagery, in IEEE IGARSS2010, IEEE International Geoscience and Remote Sensing Symposium, Honolulu, Haway, July 25-30, 2010, pp. 197-200.