Publications

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M. Shahrimie Asaari, Mertens, S., Dhondt, S., Inze, D., Wuyts, N., and Scheunders, P., “Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform”, Computers and Electronics in Agriculture, vol. 162, pp. 749-758, 2019.PDF icon shahrimie_2019.pdf (3.12 MB)
M. Shahrimie Asaari, Mishra, P., Mertens, S., Dhondt, S., Wuyts, N., and Scheunders, P., “Modeling effects of illumination and plant geometry on leaf reflectance spectra in close-range hyperspectral imaging”, in 8th WHISPERS - Evolution in Remote Sensing, Los Angeles, USA, 2016.
M. Shahrimie Asaari, Mertens, S., Dhondt, S., Inze, D., and Scheunders, P., “Analysis of Plant Stress Response Using Hyperspectral Imaging and Kernel Ridge Regression”, in 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, Singapore, 2022, vol. 829.
S. Mohd. Asaari, “Analysis of hyperspectral images for high-throughput plant phenotyping ”, 2019.
M. Shahrimie Asaari, Mertens, S., Dhondt, S., Wuyts, N., and Scheunders, P., “Detection of plant responses to drought using close-range hyperspectral imaging in a high-throughput phenotyping platform”, in IEEE Whispers 2018, Workshop on Hyperspectral Image and Signal Processing, Amsterdam, 23-26 September , 2018.
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.
M. Shahrimie Asaari, Mishra, P., Mertens, S., Dhondt, S., Inze, D., Wuyts, N., and Scheunders, P., “Close-range hyperspectral image analysis for the early detection of plant stress responses in individual plants in a high-throughput phenotyping platform”, ISPRS Journal of Photogrammetry and Remote Sensing , vol. 138, pp. 121-138, 2018.
G. Araizi-Kanoutas, Geessinck, J., Gauquelin, N., Smit, S., Verbeeck, X. H., Mishra, S. K., Bencok, P., Schlueter, C., Lee, T. - L., Krishnan, D., Fatermans, J., Verbeeck, J., Rijnders, G., Koster, G., and Golden, M. S., “Co valence transformation in isopolar LaCoO3/LaTiO3 perovskite heterostructures via interfacial engineering”, Phys. Rev. Materials, vol. 4, 2020.
B. T. Antonsen, Jiang, Y., Veraart, J., Qu, H., Nguyen, H. P., Sijbers, J., Von Hörsten, S., Johnson, A. G., and Leergaard, T. B., “Altered diffusion tensor imaging measurements in aged transgenic Huntington disease rats.”, Brain structure & function, vol. 218, no. 3, pp. 767-78, 2013.PDF icon Download paper (645.02 KB)
V. Andrejchenko, Heylen, R., Scheunders, P., Philips, W., and Liao, W., “Classification of hyperspectral images with very small training size using sparse unmixing”, in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016.
V. Andrejchenko, Heylen, R., Liao, W., Philips, W., and Scheunders, P., “MRF-based decision fusion for hyperspectral image classification”, in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018.
V. Andrejchenko, Zahiri, Z., Heylen, R., and Scheunders, P., “A spectral mixing model accounting for multiple reflections and shadow”, in IGARSS 2019, International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 286-289.
V. Andrejchenko, “Hyperspectral image mixture analysis using notions of sparsity, nonlinearity and decision fusion ”, 2020.
V. Andrejchenko, Liao, W., Philips, W., and Scheunders, P., “Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields”, Remote Sensing, vol. 11, 2019.PDF icon remotesensing-11-00624.pdf (1.5 MB)
V. Anania, Jeurissen, B., Morez, J., Buikema, A. Eline, Billiet, T., Sijbers, J., and den Dekker, A. J., “Optimal experimental design for the T2-weighted diffusion kurtosis imaging free water elimination model”, ESMRMB 2021 Online 38th Annual Scientific Meeting 7–9 October 2021. Magn Reson Mater Phy, vol. 34. pp. S54-S55, 2021.
V. Anania, Billiet, T., Jeurissen, B., Sijbers, J., and den Dekker, A. J., “Robust outlier detection for diffusion kurtosis MRI based on IRLLS”, 36th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine & Biology, vol. 32. 2019.
V. Anania, Jeurissen, B., Morez, J., Buikema, A. Eline, Billiet, T., Sijbers, J., and den Dekker, A. J., “Optimal acquisition settings for simultaneous diffusion kurtosis, free water fraction and T2 estimation”, Joint Annual Meeting ISMRM-ESMRMB. 2022.
V. Anania, Billiet, T., Jeurissen, B., Ribbens, A., den Dekker, A. J., and Sijbers, J., “Improved voxel-wise quantification of diffusion and kurtosis metrics in the presence of noise and intensity outliers”, 12th Annual Meeting ISMRM Benelux Chapter, Arnhem, The Netherlands. 2020.
V. Anania, Collier, Q., Veraart, J., Buikema, A. Eline, Vanhevel, F., Billiet, T., Jeurissen, B., den Dekker, A. J., and Sijbers, J., “Improved diffusion parameter estimation by incorporating T2 relaxation properties into the DKI-FWE model”, NeuroImage, vol. 256, p. 119219, 2022.
L. F. Alves Pereira, Janssens, E., Van Dael, M., Verboven, P., Nicolai, B., Cavalcanti, G. D. C., Tsang, I. J., and Sijbers, J., “Fast X-ray Computed Tomography via Image Completion”, in 6th Conference on Industrial Computed Tomography(iCT), Wels, Austria, 2016, pp. 1-5.
L. F. Alves Pereira, Dabravolski, A., Tsang, I. R., Cavalcanti, G. D. C., and Sijbers, J., “Conveyor belt X-ray CT using Domain Constrained Discrete Tomography”, in Sibgrapi conference on Graphics, Patterns and Images, 2014, pp. 290 - 297.PDF icon Download paper (3.45 MB)
L. F. Alves Pereira, Roelandts, T., and Sijbers, J., “Inline 3D X-ray Inspection of Food using Discrete Tomography”, in InsideFood Symposium, Leuven, Belgium, 2013.PDF icon Download paper (292.06 KB)
L. F. Alves Pereira, De Beenhouwer, J., and Sijbers, J., “The Deep Steerable Convolutional Framelet Network for Suppressing Directional Artifacts in X-ray Tomosynthesis”, in 31st European Signal Processing Conference, EUSIPCO, 2023.
L. F. Alves Pereira, De Beenhouwer, J., Kastner, J., and Sijbers, J., “Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks”, in ICTAI 2020, 2020.PDF icon Download paper (2.5 MB)

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