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J. Fatermans, De Backer, A., den Dekker, A. J., and Van Aert, S., “Chapter Six - Atom column detection”, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.
J. Fatermans, den Dekker, A. J., Müller-Caspary, K., Gauquelin, N., Verbeeck, J., and Van Aert, S., “Atom column detection from simultaneously acquired ABF and ADF STEM images”, Ultramicroscopy, vol. 219, p. 113046, 2020.
J. Fatermans, den Dekker, A. J., O'Leary, C. M., Nellist, P. D., and Van Aert, S., “Atom column detection from STEM images using the maximum a posteriori probability rule”, MC 2019, Berlin, Germany. 2019.
J. Fatermans, Van Aert, S., and den Dekker, A. J., “The maximum a posteriori probability rule for atom column detection from HAADF STEM images”, Ultramicroscopy, vol. 201, pp. 81-91, 2019.
J. Fatermans, Van Aert, S., and den Dekker, A. J., “Bayesian model-order selection in electron microscopy to detect atomic columns in noisy images”, RBSM 2016, Brussels, Belgium. p. 53, 2016.
J. Fatermans, den Dekker, A. J., Gauquelin, N., Verbeeck, J., and Van Aert, S., “Bayesian model selection for atom column detection from ABF-ADF STEM images”, Virtual Early Career EMC 2020 (online), Copenhagen, Denmark. 2020.
J. Fatermans, den Dekker, A. J., Müller-Caspary, K., Lobato, I., and Van Aert, S., “The maximum a posteriori probability rule to detect single atoms from low signal-to-noise ratio scanning transmission electron microscopy images”, IMC19, Sydney, Australia. 2018.
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L. Emsell, Leemans, A., Langan, C., Van Hecke, W., Barker, G. J., McCarthy, P., Jeurissen, B., Sijbers, J., Sunaert, S., Cannon, D. M., and McDonald, C., “Limbic and callosal white matter changes in euthymic bipolar I disorder: an advanced diffusion MRI tractography study”, Biologicial Psychiatry, vol. 73, no. 2, pp. 194-201, 2013.
T. Elberfeld, Fröhler, B., Heinzl, C., Sijbers, J., and De Beenhouwer, J., “cuPARE: Parametric Reconstruction of Curved Fibres from Glass fibre-reinforced Composites”, Nondestructive Testing and Evaluation, 2022.PDF icon Download paper (9.66 MB)
T. Elberfeld, “Parametric Fiber Analysis for Glass Fiber-reinforced Composite Tomographic Images”, 2023.PDF icon Download PhD thesis (35.09 MB)
T. Elberfeld, De Beenhouwer, J., den Dekker, A. J., Heinzl, C., and Sijbers, J., “Parametric Reconstruction of Advanced Glass Fiber-reinforced Polymer Composites from X-ray Images”, 8th Conference on Industrial Computed Tomography. Wels, Austria, 2018.PDF icon Download paper (636.09 KB)
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)
T. Elberfeld, De Beenhouwer, J., den Dekker, A. J., Heinzl, C., and Sijbers, J., “Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data - A Simulation Study”, Journal of Nondestructive Evaluation, vol. 37, no. 62, pp. 1573-4862, 2018.
T. Elberfeld, Bazrafkan, S., De Beenhouwer, J., and Sijbers, J., “Mixed-Scale Dense Convolutional Neural Network based Improvement of Glass Fiber-reinforced Composite CT Images”, 4th International Conference on Tomography of Materials & Structures. 2019.
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K. Duquesne, Van Oevelen, A., Sijbers, J., Van Paepegem, W., and Audenaert, E., “A novel Soft Tissue-Integrated Kinematic Solver for skeletal motion: Validation and applications”, Computer Methods and Programs in Biomedicine, vol. 265, 2025.
A. Duijster, De Backer, S., and Scheunders, P., “Multicomponent image restoration, an experimental study”, Lecture Notes in Computer Science, vol. 4633, pp. 58-68, 2007.
A. Duijster, De Backer, S., and Scheunders, P., “Wavelet-based Multispectral Image Restoration”, in IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008, vol. 3, pp. 79-82.
A. Duijster, De Backer, S., and Scheunders, P., “Wavelet-based Multicomponent Image Restoration”, in Wavelet Applications in Industrial Processing V, part of SPIE Optics East, Boston, MA, United States, September 9-12, 2007, vol. 6763.
A. Duijster, Scheunders, P., and De Backer, S., “Wavelet-Based EM Algorithm for Multispectral-Image Restoration”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 3892-3898, 2009.
J. Driesen and Scheunders, P., “Wavelet based segmentation of multi-component images”, in IEEE BENELUX/DSP Valley Signal Processing Symposium (SPS-DARTS) March 28-29, Antwerp, Belgium, 2006, pp. 151-154.
J. Driesen and Scheunders, P., “A Multicomponent Image Segmentation Framework”, Lecture Notes in Computer Science, vol. 5259, pp. 589-600, 2008.
J. Driesen, Thoonen, G., and Scheunders, P., “Spatial hyperspectral image classification by prior segmentation”, in Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009, Cape Town, South Africa, 2009, vol. 3, p. III-709 - III-712.
J. Driesen and Scheunders, P., “Wavelet based segmentation of multivalued images”, in SPIE Optics East, 23-26 October, Boston, Massachusetts USA, 2005, vol. 6001, pp. 13-22.
J. Driesen and Scheunders, P., “Wavelet based Filter Array Demosaicking”, in Proc. ICIP2004, IEEE International Conference on Image Processing, 24-27 october, Singapore, 2004, pp. 3311-3314.
T. Dox, Heylen, R., and Scheunders, P., “Spectral variability in a multilinear mixing model”, in IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27, 2018.

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