Prof. Dr. Aleksandra Pižurica
Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium
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Academic Career Length
28Years (1999-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications20 March 2014 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Journal Article
CDChristian Debes
Christian Debes
AMAndreas Merentitis
Andreas Merentitis
RHRoel Heremans
Roel Heremans
JHJurgen Hahn
Jurgen Hahn
NFNikolaos Frangiadakis
Nikolaos Frangiadakis
FPFabio Pacifici
Fabio Pacifici
+8
The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.
21 May 2003 in IEEE Transactions on Medical Imaging
Journal Article
Aleksandra PižuricaWPW. Philips
W. Philips
ILI. Lemahieu
I. LemahieuMAM. Acheroy
M. Acheroy
We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.
15 May 2015 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Journal Article
WLWenzhi Liao
Wenzhi Liao
XHXin Huang
Xin Huang
Frieke Marthe Bert Van CoillieSGSidharta Gautama
Sidharta GautamaAleksandra PižuricaDTDevis Tuia
Devis Tuia +5
This paper reports the outcomes of the 2014 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource remote sensing studies. In the 2014 edition, participants considered multiresolution and multisensor fusion between optical data acquired at 20-cm resolution and long-wave (thermal) infrared hyperspectral data at 1-m resolution. The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data. In this paper, the results obtained by the winners of both tracks are presented and discussed.
7 August 2002 in IEEE Transactions on Image Processing
Journal Article
Aleksandra PižuricaWPW. Philips
W. Philips
ILI. Lemahieu
I. LemahieuMAM. Acheroy
M. Acheroy
This paper presents a new wavelet-based image denoising method, which extends a "geometrical" Bayesian framework. The new method combines three criteria for distinguishing supposedly useful coefficients from noise: coefficient magnitudes, their evolution across scales and spatial clustering of large coefficients near image edges. These three criteria are combined in a Bayesian framework. The spatial clustering properties are expressed in a prior model. The statistical properties concerning coefficient magnitudes and their evolution across scales are expressed in a joint conditional model. The three main novelties with respect to related approaches are (1) the interscale-ratios of wavelet coefficients are statistically characterized and different local criteria for distinguishing useful coefficients from noise are evaluated, (2) a joint conditional model is introduced, and (3) a novel anisotropic Markov random field prior model is proposed. The results demonstrate an improved denoising performance over related earlier techniques.
4 September 2014 in IEEE Geoscience and Remote Sensing Letters
Journal Article
WLWenzhi Liao
Wenzhi Liao
Aleksandra PižuricaRBRik Bellens
Rik Bellens
SGSidharta Gautama
Sidharta GautamaWPWilfried Philips
Wilfried Philips
Nowadays, we have diverse sensor technologies and image processing algorithms that allow one to measure different aspects of objects on the Earth [e.g., spectral characteristics in hyperspectral images (HSIs), height in light detection and ranging (LiDAR) data, and geometry in image processing technologies, such as morphological profiles (MPs)]. It is clear that no single technology can be sufficient for a reliable classification, but combining many of them can lead to problems such as the curse of dimensionality, excessive computation time, and so on. Applying feature reduction techniques on all the features together is not good either, because it does not take into account the differences in structure of the feature spaces. Decision fusion, on the other hand, has difficulties with modeling correlations between the different data sources. In this letter, we propose a generalized graph-based fusion method to couple dimension reduction and feature fusion of the spectral information (of the original HSI) and MPs (built on both HS and LiDAR data). In the proposed method, the edges of the fusion graph are weighted by the distance between the stacked feature points. This yields a clear improvement over an older approach with binary edges in the fusion graph. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.
20 November 2014 in IEEE Transactions on Image Processing
Journal Article
TRTijana Ruzic
Tijana Ruzic
Aleksandra PižuricaIn this paper, we first introduce a general approach for context-aware patch-based image inpainting, where textural descriptors are used to guide and accelerate the search for well-matching (candidate) patches. A novel top-down splitting procedure divides the image into variable size blocks according to their context, constraining thereby the search for candidate patches to nonlocal image regions with matching context. This approach can be employed to improve the speed and performance of virtually any (patch-based) inpainting method. We apply this approach to the so-called global image inpainting with the Markov random field (MRF) prior, where MRF encodes a priori knowledge about consistency of neighboring image patches. We solve the resulting optimization problem with an efficient low-complexity inference method. Experimental results demonstrate the potential of the proposed approach in inpainting applications like scratch, text, and object removal. Improvement and significant acceleration of a related global MRF-based inpainting method is also evident.
About Me
Aleksandra Pižurica is a Full Professor in Statistical image modeling at Ghent University, Belgium, where she is leading the research group Artificial Intelligence and Sparse Modelling. She received a Dipl. Ing. Degree in Electrical Engineering from the University of Novi Sad, Serbia (1994), a Master of Science degree in Telecommunications from the University of Belgrade, Serbia (1997) and a Ph.D. degree in Engineering from Ghent University, Belgium (2002). She was a postdoctoral fellow (2005–2011) and a principal investigator at the Research Department of Multimedia Technology of the Flemish research institute iMinds (2009–2016). She was awarded the scientific prize “de Boelpaepe” by the Royal Academy of Science, Letters and Fine Arts of Belgium for her contributions to statistical image modeling and applications to digital painting analysis in 2015. She was co-recipient of the Best Paper Award of the IEEE GRSS Data Fusion contest in 2013 and 2014, and the David Hestenes Prize at AGACSE 2018. She is a Senior Area Editor for the IEEE TIP and formerly served as an Associate Editor of the IEEE TIP and IEEE TCSVT. She was a TPC Co-chair of the 30th EUSIPCO (in 2022), Europe Liaison for ICIP 2020, and the general chair of the Sixth International Workshop on Image Processing for Art Investigation (IP4AI) in 2018. Her Research Keywords include digital image processing, data processing, sparse representation classification and hyperspectral unmixing.