Aleksandra Pizurica
Aleksandra Pizurica
tel.: +32 9 264 31 12
research unit: Group for Artificial Intelligence and Sparse Modelling (GAIM)
website
Aleksandra Pizurica (Dipl. Ing. Degree in Electrical Engineering (1994; the University of Novi Sad); Master of Science degree in Telecommunications (1997; the University of Belgrade); Ph.D. degree in Engineering (2002; Ghent University) is Professor in statistical image modelling at Ghent University. Prof. Pizurica is a Senior Area Editor for the IEEE Transactions on Image Processing (2016 –), Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology (2016 –), and served as an Associated Editor for the IEEE Transactions on Image Processing (2012–2016). She received the Scientific Prize “de Boelpaepe” for 2013-2014, from the Royal Academy of Science, Letters and Fine Arts of Belgium. Her research is in the area of statistical modelling, probabilistic graphical models and inference, sparse coding, signal/image processing and machine learning.
Keywords: Statistical modelling, Bayesian inference, Probabilistic Graphical Models, Representation Learning, Signal Processing
- M. Panic, J. Aelterman, V. Crnojevic, and A. Pižurica, “Sparse Recovery in Magnetic Resonance Imaging with a Markov Random Field Prior,” IEEE Transactions on Medical Imaging, vol. 36, no. 10, pp. 2104 - 2115, Oct. 2017.
- S. Huang, H. Zhang and A. Pižurica, “A Robust Sparse Representation Model for Hyperspectral Image Classification,” Sensors, vol. 17, no. 9, 2017. doi:10.3390/s17092087.
- T. Ružic and A. Pižurica, “Context-aware patch-based image inpainting using Markov random field modeling,” IEEE Transactions on Image Processing, vol. 24, no. 1, pp. 444-456, Jan 2015.
- A. Pizurica and W. Philips, “Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising”, IEEE Transactions on Image Processing, vol. 15, no. 3, pp. 654-665, March 2006.
- W. Liao, A. Pižurica, W. Philips and Y. Pi, “Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 51 no. 1, pp. 184 – 198, Jan 2013.