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T. Huysmans, Sijbers, J., and Verdonk, B., Parameterization of tubular surfaces on the cylinder, Journal of the Winter School of Computer Graphics, vol. 13, pp. 97-104, 2005.PDF icon Download paper (798.29 KB)
T. Huysmans, Sijbers, J., Vanpoucke, F., and Verdonk, B., Improved Shape Modeling of Tubular Objects Using Cylindrical Parameterization, Lecture Notes in Computer Science, vol. 4091, pp. 84-91, 2006.
T. Huysmans, Sijbers, J., and Verdonk, B., Automatic Construction of Correspondences for Tubular Surfaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 636-651, 2010.PDF icon Download paper (3.46 MB)
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, e-Journal of Nondestructive Testing, vol. 28, no. 3, 2023.
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)
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.
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.
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.
K. Hufkens, Ceulemans, R., and Scheunders, P., Ecotones in vegetation ecology: methodology and definitions revisited, Ecological Research, vol. 24, pp. 977-986, 2009.
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.
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)
R. Heylen, Thanki, A., Verhees, D., Iuso, D., De Beenhouwer, J., Sijbers, J., Witvrouw, A., Haitjema, H., and Bey-Temsamani, A., 3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts, Measurement Science and Technology, vol. 33, no. 4, pp. 1-12, 2022.
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, 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)
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., 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, Parente, M., and Scheunders, P., Estimation of the number of endmembers in a hyperspectral image via the hubness phenomenon, IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 4, pp. 2191-2200, 2017.
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)
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.
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., Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratio's, IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 2, pp. 570-579, 2013.

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