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P. Scheunders, Duijster, A., and Zhang, Y., “Wavelet-based Multi/Hyperspectral Image Restoration and Fusion”, in Signal and Image Processing for Remote Sensing, C. H. Chen, Ed. Taylor and Francis, 2012, pp. 505-523.
P. Scheunders, “A genetic approach towards optimal color image quantization”, in Proc. ICIP'96, IEEE Internat. Conference on Image Processing , III, Lausanne, september 16-19, 1996, pp. 1031-1034.
P. Scheunders, “Joint quantization and error-diffusion of color images using competitive learning”, Journal of the IEE Proceedings, Vision, Image and Signal Processing, vol. 14, pp. 137-140, 1998.
P. Scheunders, Tuia, D., and Moser, G., “Contributions of Machine Learning to Remote Sensing Data Analysis”, in Comprehensive Remote Sensing, vol. 2, 2018, pp. 199-243.
P. Scheunders, “A wavelet representation of multispectral images; in: Frontiers of Remote Sensing Information Processing”, C. H. Chen, Ed. World Scientific, 2003, pp. 197-224.
P. Scheunders, “Genetic optimal quantization of gray-level and color images”, in Proceedings ACCV'95, Second Asian Conference on Computer Vision, Singapore, 5-8 december, 1995, pp. 94-98.
P. Scheunders, “Wavelet representations of multivalued images”, in Wavelet Applications in Industrial Processing, part of the International Symposium on Photonics Technologies for Robotics, Automation, and Manufacturing , 27-31 October 2003, Providence, RI USA, 2003, pp. 203-215.
P. Scheunders, “An orthogonal wavelet representation of multivalued images”, IEEE Transactions on Image Processing, vol. 12, pp. 718-725, 2003.
P. Scheunders, “A genetic c-means clustering algorithm applied to color image quantization”, Pattern Recognition, vol. 30, pp. 859-866, 1997.
P. Scheunders and De Backer, S., “Fusion and merging of multispectral images using multiscale fundamental forms”, Journal of the Optical Society of America A, vol. 18, pp. 2468-2477, 2001.
P. Scheunders and De Backer, S., “Multispectral image fusion and merging using multiscale fundamental forms”, in Proc. ICIP01, IEEE International Conference on Image Processing, Thessaloniki, Greece, 2001, pp. 902-905.
P. Scheunders, “A multivalued image wavelet representation based on multiscale fundamental forms”, IEEE Transactions on Image Processing, vol. 11, pp. 568-575, 2002.
P. Scheunders, “Denoising of multispectral images using wavelet thresholding”, in Proc. SPIE conference on Image and Signal Processing for Remote Sensing IX, part of the International Symposium on Remote Sensing, Barcelona, Spain, 2003, pp. 28-35.
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.
P. Scheunders and De Backer, S., “Wavelet denoising of multicomponent images, using Gaussian Scale Mixture models and a noise-free image as priors”, IEEE Transactions on Image Processing, vol. 16, pp. 1865-1872, 2007.
P. Scheunders, “Multivalued image segmentation based on first fundamental form”, in Proc. ICIAP, International Conference on Image Analysis and Processing, Palermo, Italy, 2001, pp. 185-190.
P. Scheunders, Van Hove, H., and Livens, S., “On the local optimality of image quantizers”, in Proc. ICPR'96 IEEE Internat. Conference on Pattern Recognition,, D, pp. 664-668, Vienna, august 25-30, 1996, pp. 664-668.
P. Scheunders, “Wavelet-based enhancement and denoising using multiscale structure tensor”, in Proceedings of the IEEE International Conference on Image Processing, Rochester NY, 2002, pp. 569-572.
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, Livens, S., Van de Wouwer, G., Vautrot, P., and Van Dyck, D., “Wavelet-based texture analysis”, Intern. Journal on Computer Science and Information Management, vol. 1, pp. 22-34, 1998.
H. Segers, Palenstijn, W. J., Batenburg, K. J., and Sijbers, J., “Segmentation of MR Images by k-space distance minimization: a proof of concept”, ESMRMB CONGRESS 2012: 29th Annual Scientific Meeting, vol. 25, no. 1. pp. 63-64, 2012.PDF icon Download abstract (221.41 KB)
H. Segers, Palenstijn, W. J., Batenburg, K. J., and Sijbers, J., “Discrete Tomography in MRI: a proof of concept”, Proc. Intl. Soc. Mag. Reson. Med, vol. 21. p. 2662, 2013.
H. Segers, Palenstijn, W. J., Batenburg, K. J., and Sijbers, J., “Discrete Tomography in MRI: a Simulation Study”, Fundamenta Informaticae, vol. 125, no. 3-4, pp. 223-237, 2013.
S. Sekar, Jonckers, E., Verhoye, M., Willems, R., Veraart, J., Van Audekerke, J., Couto, J., Giugliano, M., Wuyts, K., Dedeurwaerdere, S., Sijbers, J., Mackie, C., Ver Donck, L., Steckler, T., and Van Der Linden, A., “Subchronic memantine induced concurrent functional disconnectivity and altered ultra-structural tissue integrity in the rodent brain: revealed by multimodal MRI.”, Psychopharmacology, vol. 227, no. 3, pp. 479-91, 2013.
L. Serbruyns, Leunissen, I., Huysmans, T., Cuypers, K., Meesen, R. L., van Ruitenbeek, P., Sijbers, J., and Swinnen, S. P., “Subcortical volumetric changes across the adult lifespan: subregional thalamic atrophy accounts for age-related sensorimotor performance declines”, Cortex, vol. 65, pp. 128-138, 2015.PDF icon paper (1.35 MB)

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