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Journal Article
G. Thoonen, Mahmood, Z., Peeters, S., and Scheunders, P., “Multisource classification of color and hyperspectral images using color attribute profiles and composite decision fusion”, Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, vol. 5, pp. 510 - 521, 2012.
B. Jeurissen, Tournier, J. - D., Dhollander, T., Connelly, A., and Sijbers, J., “Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data”, NeuroImage, vol. 103, pp. 411–426, 2014.
B. Jeurissen and Szczepankiewicz, F., “Multi-tissue spherical deconvolution of tensor-valued diffusion MRI.”, Neuroimage, vol. 245, p. 118717, 2021.
P. Scheunders, “A multivalued image wavelet representation based on multiscale fundamental forms”, IEEE Transactions on Image Processing, vol. 11, pp. 568-575, 2002.
E. Janssens, De Beenhouwer, J., Van Dael, M., De Schryver, T., Van Hoorebeke, L., Verboven, P., Nicolai, B., and Sijbers, J., “Neural network Hilbert transform based filtered backprojection for fast inline X-ray inspection”, Measurement Science and Technology, vol. 29, no. 3, 2018.PDF icon Download paper (3.4 MB)
R. F. Kooy, Reyniers, E., Verhoye, M., Sijbers, J., Cras, P., Oostra, B. A., Willems, P. J., and Van Der Linden, A., “Neuroanatomy of the fragile X knockout mouse brain studied using in vivo high resolution Magnetic Resonance Imaging (MRI)”, European Journal of Human Genetics, vol. 7, pp. 526-532, 1999.PDF icon ejhg99.pdf (261.08 KB)
H. K. Jenssen, Oberlander, B. C., De Beenhouwer, J., Sijbers, J., and Verwerft, M., “Neutron radiography and tomography applied to fuel degradation during ramp tests and loss of coolant accident tests in a research reactor”, Progress in Nuclear Energy, vol. 72, pp. 55-62, 2014.
S. Verwulgen, Lacko, D., Vleugels, J., Vaes, K., Danckaers, F., De Bruyne, G., and Huysmans, T., “A new data structure and workflow for using 3D anthropometry in the design of wearable products”, International Journal of Industrial Ergonomics, vol. 64, pp. 108 - 117, 2018.
J. Rajan, den Dekker, A. J., and Sijbers, J., “A new non local maximum likelihood estimation method for Rician noise reduction in Magnetic Resonance images using the Kolmogorov-Smirnov test”, Signal Processing, vol. 103, pp. 16-23, 2014.
J. Rajan, Poot, D. H. J., Juntu, J., and Sijbers, J., “Noise measurement from magnitude MRI using local estimates of variance and skewness.”, Physics in medicine and biology, vol. 55, no. 16, pp. N441-9, 2010.PDF icon Download paper (219.85 KB)
B. Rasti, Scheunders, P., Ghesami, P., Licciardi, G., and Chanussot, J., “Noise reduction in hyperspectral imagery: overview and application”, Remote Sensing , vol. 10, no. 3, p. 482, 2018.
Y. Zhang, De Backer, S., and Scheunders, P., “Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 3834-3843, 2009.
M. Shahrimie Asaari, Mertens, S., Verbraeken, L., Dhondt, S., Inze, D., Koirala, B., and Scheunders, P., “Non-Destructive Analysis of Plant Physiological Traits Using Hyperspectral Imaging: A Case Study on Drought Stress”, Computers and Electronics in Agriculture, vol. 195, no. 106806, 2022.PDF icon 1-s2.0-s0168169922001235-main.pdf (5.53 MB)
M. Shahrimie Asaari, Mertens, S., Verbraeken, L., Dhondt, S., Inze, D. G., Koirala, B., and Scheunders, P., “Non-destructive analysis of plant physiological traits using hyperspectral imaging: A case study on drought stress”, Computers and Electronics in Agriculture, vol. 195, 2022.
T. Van De Looverbosch, Raeymaekers, E., Verboven, P., Sijbers, J., and Nicolai, B., “Non-destructive internal disorder detection of Conference pears by semantic segmentation of X-ray CT scans using deep learning”, Expert Systems with Applications, vol. 176, no. 114925, pp. 1-12, 2021.
T. Van De Looverbosch, Bhuiyan, H. Rahman, Verboven, P., Dierick, M., Van Loo, D., De Beenhouwer, J., Sijbers, J., and Nicolai, B., “Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning”, Food Control, vol. 113, no. 107170, pp. 1-13, 2020.
S. De Backer, Naud, A., and Scheunders, P., “Non-linear Dimensionality Reduction Techniques for Unsupervised Feature Extraction”, Pattern Recognition Letters, vol. 19, pp. 711-720, 1998.
R. Heylen, Andrejchenko, V., Zahiri, Z., Parente, M., and Scheunders, P., “Nonlinear hyperspectral unmixing with graphical models”, IEEE Transaction on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4844-4856, 2019.PDF icon published.pdf (3.15 MB)
P. Scheunders, De Backer, S., and Naud, A., “Non-linear mapping for feature extraction”, Lecture Notes in Computer Science, vol. 1451, pp. 823-830, 1998.
R. Heylen, Burazerovic, D., and Scheunders, P., “Nonlinear spectral unmixing by geodesic simplex volume maximization”, IEEE Journal of Selected Topics in Signal Processing, vol. 5, pp. 534-542, 2011.
B. Koirala, Rasti, B., Bnoulkacem, Z., and Scheunders, P., “Nonlinear Spectral Unmixing Using Bézier Surfaces”, IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-16, 2024.PDF icon nonlinear_spectral_unmixing_using_bezier_surfaces.pdf (9.66 MB)
R. Heylen, Scheunders, P., Gader, P., and Rangarajan, A., “Nonlinear unmixing by using different metrics in a linear unmixing chain”, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015.
J. Rajan, Veraart, J., Van Audekerke, J., Verhoye, M., and Sijbers, J., “Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images.”, Magnetic resonance imaging, vol. 30, no. 10, pp. 1512-8, 2012.
J. Rajan, Veraart, J., Van Audekerke, J., Verhoye, M., and Sijbers, J., “Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images”, Magnetic Resonance Imaging, vol. 30, no. 10, pp. 1512-1518, 2012.PDF icon Download full paper (1.11 MB)
P. V. Sudeep, Palanisamy, P., Kesavadas, C., Sijbers, J., den Dekker, A. J., and Rajan, J., “A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps”, Signal Image and Video Processing, vol. 11, no. 5, pp. 913-920, 2017.

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