Publications

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Author [ Type(Desc)] Year
Conference Paper
J. Sijbers, Michiels, I., Verhoye, M., Van Audekerke, J., Van Der Linden, A., and Van Dyck, D., Automatic, adaptive filter for MRI related artefacts in simultaneously recorded EEG/MRI data, in Third Meeting Belgian Society for Neuroscience, 1999.
J. Sijbers, Vanrumste, B., Van Hoey, G., Boon, P., Verhoye, M., Van Der Linden, A., and Van Dyck, D., Automatic detection of EEG electrode markers on 3D MR data, in SPIE Medical Imaging: Image Processing, San Diego CA, USA, 2000, vol. 3979, pp. 1476-1481.
J. Sijbers, Michiels, I., Van Audekerke, J., Verhoye, M., and Van Der Linden, A., Automatic EEG signal restoration during simultaneous EEG/MR acquisitions, in SPIE Medical Imaging: Image Processing, San Diego, California, USA, 2000, vol. 3979, pp. 1482-1491.
W. D'haes and Rodet, X., Automatic Estimation of Control Parameters: An Instance-Based Learning Approach, in International Computer Music Conference (ICMC), Havana, Cuba, 2001.
D. H. J. Poot, Sijbers, J., den Dekker, A. J., and Pintjens, W., Automatic estimation of the noise variance from the histogram of a magnetic resonance image, in IEEE/EBMS Benelux Symposium proceedings, 2006, pp. 135-138.
F. Danckaers, Scataglini, S., Haelterman, R., Van Tiggelen, D., Huysmans, T., and Sijbers, J., Automatic Generation of Statistical Shape Models in Motion, in Advances in Human Factors in Simulation and Modeling (AHFE 2018), Cham, 2019, vol. 780, pp. 170–178.
J. Cant, Behiels, G., and Sijbers, J., Automatic geometric calibration of chest tomosynthesis using data consistency conditions, in The 4th International Conference on Image Formation in X-Ray Computed Tomography, Salt Lake City, Utah, USA, 2016, pp. 161-164.PDF icon Download paper (1.9 MB)
K. J. Batenburg and Sijbers, J., Automatic local thresholding of tomographic reconstructions based on the projection data, in SPIE Medical Imaging, San Diego, CA, USA, 2008, vol. 6913, p. 69132.PDF icon Download full paper (857.39 KB)
K. J. Batenburg, Sijbers, J., and Van de Casteele, E., Automatic multiple threshold scheme for segmentation of tomograms, in Proceedings of the Biomedical Engineering IEEE/EMBS Benelux Symposium, Brussels, Belgium, 2006, vol. 2, pp. 143-146.
K. J. Batenburg and Sijbers, J., Automatic multiple threshold scheme for segmentation of tomograms, in Proceedings of SPIE Medical Imaging: Physics of Medical Imaging, San Diego, CA, USA, 2007.PDF icon Download full paper (630.82 KB)
E. Bettens, Scheunders, P., Sijbers, J., Van Dyck, D., and Moens, L., Automatic segmentation and modeling of two-dimensional electrophoresis gels, in Proceedings of IEEE International Conference on Image Processing, Lausanne, Switserland, 1996, vol. 2, pp. 665-668.
Z. Mahmood, Thoonen, G., and Scheunders, P., Automatic threshold selection for morphological attribute profiles, in IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27, 2012, pp. 4946-4949.
Y. Zhang, De Backer, S., and Scheunders, P., Bayesian fusion of multispectral and hyperspectral image in wavelet domain, in IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, Boston, United States, 6-11 July, 2008, pp. 69-72.
P. Scheunders, Bayesian techniques for multi/hyperspectral image processing, in 4th IEEE Conference on Industrial Electronics and Applications, Xi’an, China, May 25-27, 2009, pp. 1-10.
P. Scheunders, De Backer, S., Pizurica, A., Huysmans, B., and Philips, W., Bayesian Wavelet-based Denoising of Multicomponent Images, in Proceedings Wavelet Applications in Industrial Processing V, part of SPIE Optics East, Boston, MA, 9-12 september, Vol. 6763-OK (12 pages), 2007.
V. Nguyen, De Beenhouwer, J., Bazrafkan, S., Hoang, A. - T., Van Wassenbergh, S., and Sijbers, J., BeadNet: a network for automated spherical marker detection in radiographs for geometry calibration, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020, pp. 518-521.PDF icon Download paper (2.16 MB)
J. Juntu, Sijbers, J., Van Dyck, D., and Gielen, J. L., Bias Field Correction for MRI Images, in Proceedings of the 4th International Conference on Computer Recognition Systems (CORES05), Rydzyna Castle, Poland, 2005, pp. 543-551.
E. Van de Casteele, Van Dyck, D., Sijbers, J., and Raman, E., A bimodal energy model for correcting beam hardening artefacts in X-ray tomography, in IEEE 29th Annual Northeast Bioengineering Conference2003 IEEE 29th Annual Proceedings of Bioengineering Conference, Newark, NJ, USA, 2003, pp. 57 - 58.
B. Rasti and Koirala, B., Blind Nonlinear Unmixing For Intimate Mixtures Using Hapke Model And CNN, in Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2022, pp. 1-5.PDF icon whispers_2022_hapkecnn.pdf (1.93 MB)
E. Van de Casteele, Batenburg, K. J., Salmon, P., and Sijbers, J., Bone segmentation using discrete tomography, in Micro-CT User Meeting, 2011, pp. 178-183.PDF icon Download paper (107.53 KB)
B. Rasti, Koirala, B., Scheunders, P., Ghamisi, P., and Gloaguen, R., Boosting Hyperspectral Image Unmixing using Denoising: Four Scenarios, in IGARSS 2021, International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 2021.
B. Rasti, Koirala, B., Scheunders, P., Ghamisi, P., and Gloaguen, R., BOOSTING HYPERSPECTRAL IMAGE UNMIXING USING DENOISING: FOUR SCENARIOS, in IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021.
A. J. den Dekker, Sijbers, J., Bos, R., and Smolders, A., Brain activation detection from functional magnetic resonance imaging data using likelihood based hypothesis tests, in Abstracts of the 24th Benelux Meeting on Systems and Control, Houffalize, Belgium, 2005.
A. J. den Dekker and Sijbers, J., Brain activation detection from magnitude fMRI data using a generalized likelihood ratio test, in Abstracts of the 23rd Benelux Meeting on Systems and Control, Helvoirt, The Netherlands, 2004.
A. Presenti, Sijbers, J., den Dekker, A. J., and De Beenhouwer, J., CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019, pp. 1-5.PDF icon ict2019_full_paper_55.pdf (216.2 KB)

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