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F. Danckaers, Huysmans, T., Van Dael, M., Verboven, P., Nicolai, B., and Sijbers, J., Building 3D Statistical Shape Models of Horticultural Products, Food and Bioprocess Technology, vol. 10, no. 11, pp. 2100-2112, 2017.PDF icon Download paper (1.73 MB)
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.
F. Danckaers, Huysmans, T., Van Dael, M., Verboven, P., Nicolai, B., and Sijbers, J., Building a Statistical Shape Model of the Apple from Corresponded Surfaces, Chemical Engineering Transactions, vol. 44, pp. 49-54, 2015.PDF icon Download paper (696.94 KB)
F. Danckaers, The Development of 3D Statistical Shape Models for Diverse Applications, 2019.
F. Danckaers, Van Houtte, J., Booth, B. G., Verstreken, F., and Sijbers, J., Statistical shape and pose model of the forearm for custom splint design, in IEEE International Symposium on Biomedical Imaging (ISBI), 2021.
F. Danckaers, Huysmans, T., and Sijbers, J., Building 3D Statistical Shape Models of Vegetables and Fruit, National Symposium for Applied Biological Sciences 2016 (NSABS). 2016.
F. Danckaers, Huysmans, T., Lacko, D., Ledda, A., Verwulgen, S., Van Dongen, S., and Sijbers, J., Correspondence Preserving Elastic Surface Registration with Shape Model Prior, in International Conference of Pattern Recognition, 2014, vol. 22, pp. 2143-2148.PDF icon paper (550.57 KB)
F. Danckaers, Lacko, D., Verwulgen, S., De Bruyne, G., Huysmans, T., and Sijbers, J., A Combined Statistical Shape Model of the Scalp and Skull of the Human Head, in 8th International Conference on Applied Human Factors and Ergonomics (AHFE 2017), Los Angeles, California, USA, 2018, vol. 591, pp. 538-548.PDF icon Download paper (934.68 KB)
M. Das and Liang, Z., Approximated transport-of-intensity equation for coded-aperture x-ray phase-contrast imaging, Optics Letters, vol. 39, no. 18, p. 5395, 2014.
A. De Backer, Fatermans, J., den Dekker, A. J., and Van Aert, S., Chapter Two - Statistical parameter estimation theory: principles and simulation studies, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.
S. De Backer and Scheunders, P., Texture segmentation by frequency-sensitive elliptical competitive learning, in Proc. ICIAP99, International Conference on Image Analysis and Processing , Venice, Italy, september 27-29, 1999, pp. 64-69.
S. De Backer, Kempeneers, P., Debruyn, W., and Scheunders, P., Wavelet Based Hyperspectral Data Analysis for Vegetation Stress Classification, in Proc. of Advanced Concepts for Intelligent Vision Systems, Brussels, Belgium, 2004, pp. 387-391.
A. De Backer, Fatermans, J., den Dekker, A. J., and Van Aert, S., Chapter Three - Efficient fitting algorithm, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.
S. De Backer and Scheunders, P., Enhancement of fMRI image series with the aid of an anatomical image, in ISBI’06, IEEE International Symposium on Biomedical Imaging, Arlington, Virginia, april 6-9, 2006, pp. 1052-1055.
A. De Backer, Fatermans, J., den Dekker, A. J., and Van Aert, S., Chapter Four - Atom counting, in Advances in Imaging and Electron Physics,, vol. 217, Science Direct Elsevier, 2021.
S. De Backer, Naud, A., and Scheunders, P., Non-linear Dimensionality Reduction Techniques for Unsupervised Feature Extraction, Pattern Recognition Letters, vol. 19, pp. 711-720, 1998.
S. De Backer, Kempeneers, P., Debruyn, W., and Scheunders, P., Classification of Dune Vegetation from Remotely Sensed Hyperspectral Images, in Image Analysis and Recognition Proc. of International Conference on Image Analysis and Recognition, Porto, Portugal, 2004, pp. 497-503.
A. De Backer, Fatermans, J., den Dekker, A. J., and Van Aert, S., Chapter Eight - General conclusions and future perspectives, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.
A. De Backer, van den Bos, K. H. W., Van den Broek, W., Sijbers, J., and Van Aert, S., StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron microscopy images, Ultramicroscopy, vol. 171, pp. 104–116, 2016.
A. De Backer, Fatermans, J., den Dekker, A. J., and Van Aert, S., Chapter One - Introduction, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.
S. De Backer and Scheunders, P., Texture segmentation by frequency-sensitive elliptical competitive learning, Image and Vision Computing, vol. 19, pp. 639-648, 2001.
S. De Backer, Unsupervised Pattern Recognition - Dimensionality Reduction and Classification, University of Antwerp, Antwerp, 2002.
S. De Backer, Pizurica, A., Huysmans, B., Philips, W., and Scheunders, P., Denoising of Multicomponent Images Using Wavelet Least-Squares Estimators, Image and Vision Computing, vol. 26, pp. 1038-1051, 2008.
S. De Backer, Cornelissen, F., Lemeire, J., Nuydens, R., Meert, T., Schelkens, P., and Scheunders, P., Mosiacing of Fibered Fluorescence Microscopy Video, Lecture notes in Computer Science, vol. 5259, pp. 915-923, 2008.
A. De Backer, Fatermans, J., den Dekker, A. J., and Van Aert, S., Chapter Five - Optimal experiment design for nanoparticle atom counting from ADF STEM images, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.

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