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R. Heylen, Parente, M., and Scheunders, P., “Estimation of the intrinsic dimensionality in hyperspectral imagery via the hubness phenomenon”, in LVA ICA 2017, International conference on latent variable analysis and signal separation, Grenoble, France, February 21-23, Lecture Notes in Computer Science, 2017, vol. 10169.
R. Heylen, Akhter, M. A., and Scheunders, P., “On using projection onto convex sets for solving the hyperspectral unmixing problem”, IEEE Geoscience and Remote Sensing Letters, 2013.
R. Heylen and Scheunders, P., “Multi-dimensional pixel purity index for convex hull estimation and endmember extraction”, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 7, pp. 4059-4069, 2013.
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.
R. Heylen and Scheunders, P., “Nonlinear unmixing with a multilinear mixing model”, in IEEE Whispers 2015, Workshop on Hyperspectral Image and Signal Processing, June 2-5, Tokyo, 2015.
R. Heylen, Zare, A., Gader, P., and Scheunders, P., “Hyperspectral unmixing with endmember variability via alternating angle minimization”, IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 8, pp. 4983-4993, 2016.
R. Heylen and Scheunders, P., “Hyperspectral unmixing using an active set algorithm”, in IEEE ICIP 2014, International Conference on Image Processing, October 27-30, Paris, France , 2014.
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)
M. Das and Liang, Z., “SPIE ProceedingsSingle-step, quantitative x-ray differential phase contrast imaging using spectral detection in a coded aperture setup”, in SPIE Medical ImagingMedical Imaging 2015: Physics of Medical Imaging, Orlando, Florida, United States, 2015, vol. 9412, p. 941252.
M. Das, Kandel, B., Park, C. Soo, and Liang, Z., “SPIE ProceedingsEnergy calibration of photon counting detectors using x-ray tube potential as a reference for material decomposition applications”, in SPIE Medical ImagingMedical Imaging 2015: Physics of Medical Imaging, Orlando, Florida, United States, 2015, vol. 9412, p. 941214.
S. Hosseinnejad, Bosch, E. G. T., Kohr, H., Lazić, I., Zharinov, V., Franken, E., Sijbers, J., and De Beenhouwer, J., “3D atomic resolution tomography from iDPC-STEM images using multiple atom model prior”, Microscopy Conference. 2021.PDF icon Download abstract (534.35 KB)
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.
T. Hu, Li, W., Liu, N., Tao, R., Zhang, F., and Scheunders, P., “Hyperspectral Image Restoration Using Adaptive Anisotropy Total Variation and Nuclear Norms”, IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 2, pp. 1516-1533, 2021.PDF icon tgrs_2020.pdf (5.71 MB)
K. Hufkens, Scheunders, P., and Ceulemans, R., “Validation of the sigmoid wave curve fitting algorithm on a forest-tundra ecotone in the Northwest Territories, Canada”, Ecological Informatics, vol. 4, pp. 1-7, 2009.
K. Hufkens, Thoonen, G., Vanden Borre, J., Scheunders, P., and Ceulemans, R., “Habitat reporting of a heathland site: Classification probabilities as additional information, a case study”, Ecological Informatics, vol. 5, pp. 248 - 255, 2010.
K. Hufkens, Ceulemans, R., and Scheunders, P., “Estimating the ecotone width in patchy ecotones using a sigmoid wave approach”, Ecological Informatics, vol. 3, pp. 97-104, 2008.
K. Hufkens, Ceulemans, R., and Scheunders, P., “Ecotones in vegetation ecology: methodology and definitions revisited”, Ecological Research, vol. 24, pp. 977-986, 2009.
S. Huijs, Huysmans, T., De Jong, A., Arnout, N., Sijbers, J., and Bellemans, J., “Principal component analysis as a tool for determining optimal tibial baseplate geometry in modern TKA design”, Acta Orthop Belg, vol. 84, no. 4, pp. 452-460, 2018.
C. C. Hung, Coleman, T., and Scheunders, P., “Using genetic differential competitive learning for unsupervised training in multispectral image classification systems”, in Proceedings IEEE International Conference on Systems, Man, and Cybernetics , San Diego, California, October 11-14, 1998, pp. 4482-4485.
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.
C. C. Hung, Scheunders, P., Pham, M., Su, M. C., and Coleman, T., “Using Intelligent Optimization Techniques in the K-means Algorithm for Multispectral Image Classification”, International Journal of Fuzzy Systems, vol. 6, pp. 107-117, 2004.
Y. Huybrechts, De Ridder, R., De Samber, B., Boudin, E., Tonelli, F., Knapen, D., Schepers, D., De Beenhouwer, J., Sijbers, J., Forlino, A., Coucke, P., P. Witten, E., Kwon, R., Willaert, A., Hendrickx, G., and Van Hul, W., “The sqstm1tmΔUBA zebrafish model, a proof-of-concept in vivo model for Paget’s disease of bone?”, Bone Reports, vol. 16, no. 101483, pp. 75-76, 2022.
Y. Huybrechts, De Ridder, R., Bergen, D., De Samber, B., Boudin, E., Tonelli, F., Knapen, D., Vergauwen, L., Schepers, D., Van Dijck, E., Tong, Q., Verhulst, A., De Beenhouwer, J., Sijbers, J., Hammond, C., Forlino, A., Mortier, G., Coucke, P., P Witten, E., Kwon, R. Young, Willaert, A., Hendrickx, G., and Van Hul, W., “Loss of the Ubiquitin-Associated Domain of sqstm1/p62 in Zebrafish Causes a Phenotype Resembling Paget’s Disease of Bone”, Calcified Tissue International, vol. 116, no. 1, pp. 1-15, 2025.
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. Huyge, Vanthienen, P. - J., Six, N., Sijbers, J., and De Beenhouwer, J., “Adapting an XCT-scanner to enable edge illumination X-ray phase contrast imaging”, in e-Journal of Nondestructive Testing, 2023, vol. 28, no. 3.

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